Artificial intelligence system and method for site safety and tracking

ABSTRACT

A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

FIELD

The subject matter described herein relates to apparatuses, systems, andmethods for improving safety at worksites.

BACKGROUND

Modern worksites often expose workers to safety hazards such as thosecaused by falls, construction equipment, machinery, exposure to toxicchemicals, electrocution, fire, heat exposure, drowning, vehicles, aswell as other sources. Because the conditions in and around worksitesare frequently changing, identifying and predicting hazards arechallenging. Even when dangers are identified, ensuring that worksitepersonnel avoid the hazards commonly proves difficult.

A widespread tool for tracking personnel on job sites and duringemergencies is the T-card system, which is a manual system subject tohuman error. For example, the names on the cards might not be updated,the wrong card could be flipped, the card might not be flipped at all(for example in an emergency situation or simply because the workerforgot), or the card may be placed in an incorrect slot. When the T-cardsystem identifies that a person is missing, a rescue team is oftendispatched to search for this missing person. Thus, when the T-cardsystem produces inaccurate data, additional workers may unnecessarily beexposed to hazards and dangers at the worksite.

SUMMARY OF THE INVENTION

The present disclosed embodiments include apparatuses, systems, andmethods for identifying and mitigating safety risks at job sites,tracking personnel, and predicting and reducing the occurrence of futureworksite hazards. The system uses wearable devices, sensors, networktracking and machine learning to monitor and track human-equipmentinteraction at worksites to promote safety and performance.

In one aspect, the present invention is directed to a wearable deviceincluding: a communications module; a programmable logic controller(PLC) communicatively coupled to the communications module; and atoxicity module communicatively coupled to the PLC. The toxicity moduleincludes a toxicity detector for detecting at least one toxic gas.

In some embodiments, the device includes a screen disposed on a frontface of the wearable device; and an alert system for initiating a localaction when at least one local alert is sensed. The local actionincludes: displaying a text on the screen, initiating a vibration withinthe wearable device, illuminating the screen, and/or activating anaudible alarm. The local alert includes a toxicity exceedance, atemperature out of range, a heartrate stoppage, a heartrateirregularity, a decibel level exceedance, a moisture fault, a movementfault, and/or an oxygen fault.

In some embodiments, the communications module includes a receiver, atransceiver, and/or a transmitter. At least one component of thecommunications module operates at a frequency from about 100 MHz toabout 5.1 GHz.

In some embodiments, the toxicity module further includes: an inlet forfluidly coupling an interior of the wearable device to an exterior ofthe wearable device; an inlet guard extending across the inlet toprevent the inlet from becoming blocked or clogged; and a moisturesensor for detecting moisture in the immediate vicinity of the toxicitydetector. The toxicity detector includes a CO2 sensor, an LEL sensor, aCO sensor, an H2S sensor, a chlorine gas sensor, a hydrocarbon sensor,and/or an oxygen sensor.

In some embodiments, the alert system transmits at least one signal to anetwork based on the local alert.

In some embodiments, the device includes a camera communicativelycoupled to the PLC.

In some embodiments, the device includes a temperature sensorcommunicatively coupled to the PLC.

In some embodiments, the device includes a humidity sensorcommunicatively coupled to the PLC.

In some embodiments, the device includes an accelerometercommunicatively coupled to the PLC.

In some embodiments, the device includes a vibrating toolcommunicatively coupled to the PLC.

In some embodiments, the device includes a camera communicativelycoupled to the PLC, the local action includes capturing at least oneimage via the camera, and the local action is initiated upon receiving asignal at the communications module from at least one network.

In some embodiments, the PLC includes at least one local interfaceallowing a user to control the wearable device and/or program thewearable device.

In some embodiments, the device includes a storage modulecommunicatively coupled to the PLC and including memory. The storagemodule includes a storage capacity between about 1 MB and about 2 TB.

In some embodiments, at least a portion of the memory in the storagemodule is removable from the wearable device.

In another aspect, the present invention is directed to a wearabledevice including: a toxicity module which includes a toxicity detectorfor detecting at least one toxic gas; and a communications module forcoupling to at least one electronic device. The communications moduleincludes a USB port for connecting to the electronic device via one ormore USB connectors and/or a transceiver for wirelessly communicatingwith the electronic device.

In another aspect, the present invention is directed to a system forenhancing safety at a worksite including: more than one sensor forsensing parameters relating to one or more safety conditions; and atleast one electronic device communicatively coupled to the sensors, theelectronic device tracking the parameters relating to one or more safetyconditions. The sensors include at least one toxicity detector fordetecting at least one toxic gas.

In some embodiments, the electronic device is a smartphone.

In some embodiments, the electronic device is communicatively andelectrically coupled to the toxicity detector via at least one USBconnector.

In some embodiments, the sensors include at least one heartrate monitorcommunicatively coupled to the electronic device.

In some embodiments, the heartrate monitor is disposed within awristwatch and/or coupled to a body part of at least one site worker viaone or more straps.

In some embodiments, the system includes at least one headsetcommunicatively coupled to the electronic device. The headset includesone or more speakers, and the headset at least partially blocks ambientnoise.

In some embodiments, the sensors include a humidity sensor and/or atemperature sensor.

In another aspect, the present invention is directed to a worksitesafety tracking system including: at least one network including aplurality of communicatively coupled electronic devices; and at leastone mobile tracking device communicatively coupled to the network, whereat least one alert is generated by the network based on a location ofthe tracking device within the worksite.

In some embodiments, the mobile tracking device includes at least onewearable device worn by at least one worker at the worksite.

In some embodiments, the at least one mobile tracking device furtherincludes at least one RFID tag coupled to at least one piece ofequipment at the worksite, the equipment being a vehicle, a crane, aforklift, a piece of equipment, and/or a tool.

In some embodiments, the mobile tracking device further includes atleast one boundary marker used for marking a boundary of at least onezone at the worksite, the boundary marker operating within two or morefrequency bandwidths.

In some embodiments, the alert is generated by the network based on thelocation of the wearable device within the zone.

In some embodiments, the alert is transmitted to the wearable device.

In some embodiments, the system includes at least one control consolecommunicatively coupled to the network, where the mobile tracking deviceis coupled to one or more site workers and/or one or more pieces ofequipment, and where a site supervisor views one or more locations ofthe site workers and the pieces of equipment based on data received atthe network from the mobile tracking device.

In some embodiments, the alert is based on at least one gas leakdetected by the wearable device.

In some embodiments, the location of the tracking device is determinedvia GPS.

In some embodiments, the system includes a plurality of zones defined bythe boundary markers, where the network tracks, on a real-time or nearreal-time basis, how many workers are within each zone.

In some embodiments, the wearable device transmits data to the networkwhen at least one of the following conditions is met: the wearabledevice senses toxic gas via a toxicity module, a predetermined timeperiod has elapsed, a data buffer of the wearable device has reached astorage limit, and/or the worker directs the wearable device to transmitdata to the network.

In some embodiments, the wearable device transmits data to the networkat least once every 15 minutes.

In some embodiments, the wearable device continuously transmits locationdata to the network.

In another aspect, the present invention is directed to a method ofsending and receiving data including: receiving, at a network, at leastone data input; performing, at the network, at least one preprocessingstep on the data input; logging, at the network, the data input,following the preprocessing step; updating at least one neural networkstored on the network based on the data input, following thepreprocessing step; transmitting the data input to a decision modulestored on the network; and initiating, at the decision module, at leastone action based on the data input.

In some embodiments, the preprocessing step includes decompressing thedata input, parsing the data input, collating the data input, and/orfiltering the data input.

In some embodiments, the method includes characterizing the data inputfollowing the preprocessing step, where characterizing the data inputincludes tagging the data as: accident data, assigned-task update (orstatus) data, sub-task update (or status) data, worksite instrumentationdata, worksite zone data, worksite alert data, and/or wearable devicedata.

In some embodiments, the method includes making at least onerecommendation, at the decision module, for the action; transmitting, atthe decision module, the recommendation to a site supervisor; andconfirming, at the site supervisor, the recommendation from the decisionmodule.

In some embodiments, the action includes: transmitting at least onealert to at least one remote device, activating at least one camerafunction on at least one wearable device, deploying at least one rescuecrew to at least one emergency zone, causing the wearable device tovibrate, causing a screen on the device to become illuminated, causing ascreen on the device to flash, causing an audible alarm to sound at thedevice, and/or causing at least one text message to be displayed on thewearable device.

In another aspect, the present invention is directed to a worksiteproductivity tracking system including: at least one wearable deviceworn by at least one worker at the worksite; at least one networkcommunicatively coupled to the wearable device; at least one zone at theworksite, the zone defined by one or more boundaries that areelectronically defined by the network; and at least one task assigned tothe worker, the task being associated with the zone, where an alert isgenerated in the network if the worker is not physically located in thezone.

In some embodiments, the worker provides at least one status update tothe network via the wearable device.

In some embodiments, the worker provides the status update via at leastone voice command received by the wearable device, where the statusupdate relates to a sub-task of the task assigned to the worker.

In another aspect, the present invention is directed to amachine-learning ecosystem including: at least one data input including:at least one input parameter and at least one output parameter. Theecosystem also includes: at least one prediction model based on the datainput and relating the output parameter to the input parameter; at leastone correlation module for building the prediction model and performingat least one threshold check on the prediction model to assess therobustness of the prediction model; and a decision modulecommunicatively coupled to the correlation module and receiving theprediction model from the correlation module. Based on at least oneverification check at the decision module, a confirmation, a deferral,and/or a rejection of the prediction model is sent from the decisionmodule to the correlation module.

In some embodiments, the verification check includes calculating anaggregate score including: an r-squared value, a confidence interval, anumber of data points within the data input, a number of data inputsused by the correlation module, an underlying data quality of the datainput, a curve-fitting equation, and/or a transfer function.

In some embodiments, the prediction model includes at least onerecommendation proposing one or more actions to improve a productivityof a worksite and/or at least one safety metric of a worksite.

In some embodiments, the ecosystem includes at least one communicationsmodule communicatively coupled to both the decision module and thecorrelation module, the communications module receiving the data inputfrom at least one data source, where the communications module includesat least one tri-band transceiver for transmitting and receiving datawithin three or more different frequency bands.

In some embodiments, the communications module performs at least onepre-processing step on the data input, the pre-processing stepincluding: parsing the data input, collating the data input,characterizing the data input, filtering the data input, and/ordecompressing the data input.

In some embodiments, the ecosystem includes: a site supervisorcommunicatively coupled to the decision module and including at leastone control console including at least one human interface, where thesite supervisor affirms at least one prediction model confirmed by thedecision module.

In some embodiments, the site supervisor gradually transitions fromhuman-authority to machine-authority as a confidence level of theprediction model generated by the correlation module increases.

In some embodiments, the ecosystem includes: at least one communicationsmodule communicatively coupled to both the decision module and thecorrelation module; and at least one wearable device communicativelycoupled to the communications module. The data input includes one ormore data points form the wearable device.

In some embodiments, the wearable device includes: at least one toxicitysensor; and at least one microphone.

In some embodiments, the wearable device includes: a temperature sensor,an accelerometer, a humidity sensor, a vibration tool, a heartratemonitor, a PLC, a USB port, a speaker, and/or a camera.

In some embodiments, the microphone records verbal communications thatare transmitted by the wearable device to the communications module, andthe correlation module uses the verbal communications as metadata forrefining the prediction model.

In some embodiments, the ecosystem includes at least one communicationsmodule communicatively coupled to both the decision module and thecorrelation module; and at least one data warehouse communicativelycoupled to both the communications module and the correlation module,where the data warehouse includes enterprise data from at least oneworksite.

In some embodiments, the correlation module includes: at least onegraphics processing unit (GPU), at least one field programmable gatearray (FPGA), and/or at least one application-specific integratedcircuit (ASIC).

In some embodiments, the correlation module further includes more thanone application-specific integrated circuit (ASIC) disposed in aclimate-controlled environment comprising a temperature not exceeding 95degrees F., where at least one application-specific integrated circuit(ASIC) accommodates an input power from about 500 W to about 3000 W, andan input voltage from about 110V to about 240V.

In some embodiments, the decision module includes at least one centralprocessing unit (CPU).

In another aspect, the present invention is directed to a method ofbuilding a correlation matrix including: providing, at a correlationmodule, at least one data input including at least one input parameterand at least one output parameter; building, at the correlation module,at least one correlation relating the output parameter to the inputparameter; performing, at the correlation module, at least one thresholdcheck on the correlation; making, at the correlation module, at leastone recommendation based on the correlation; transmitting therecommendation to a decision module; evaluating, at the decision module,the recommendation; transmitting feedback from the decision module tothe correlation module, the feedback including a confirmation, arejection, and/or a deferral; and initiating at least one action basedon the evaluation, at the decision module, of the recommendation.

In some embodiments, the action is directed to improving theproductivity of a worksite and/or improving the safety of a worksite.

In some embodiments, the method includes deploying the correlationmatrix at a worksite, where initiating the action includes: sending outan alert, dispatching one or more rescue crews to an emergency area ofthe worksite, reassigning crew to a different task at the worksite,and/or repositioning equipment at the worksite.

In some embodiments, the method includes refining, at the correlationmodule, the correlation based on the feedback received from the decisionmodule.

In some embodiments, the method includes refining, at the correlationmodule, the correlation based on the data input, where the data inputincludes one or more verbal recordings received from at least onewearable device.

In some embodiments, evaluating the recommendation further includesperforming at least one verification check at the decision module.

In another aspect, the present invention is directed to amachine-learning ecosystem including: a correlation module for buildingat least one prediction model based on at least one data input includingat least one input parameter and at least one output parameter. Theprediction model relates the output parameter to the input parameter.The correlation module performs at least one threshold check on theprediction model to assess the robustness of the prediction model. Theecosystem also includes a decision module communicatively coupled to thecorrelation module, the decision module receiving the prediction modelfrom the correlation module. Based on at least one verification check atthe decision module, a confirmation, a deferral, and/or a rejection ofthe prediction model is sent from the decision module to the correlationmodule.

Throughout the description, where an apparatus, systems or compositionsare described as having, including, or comprising specific components,or where methods are described as having, including, or comprisingspecific steps, it is contemplated that, additionally, there aresystems, apparatuses or compositions of the present invention thatconsist essentially of, or consist of, the recited components, and thatthere are methods according to the present invention that consistessentially of, or consist of, the recited processing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial as long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The following description is for illustration and exemplification of thedisclosure only, and is not intended to limit the invention to thespecific embodiments described.

The mention herein of any publication, for example, in the Backgroundsection, is not an admission that the publication serves as prior artwith respect to any of the present claims. The Background section ispresented for purposes of clarity and is not meant as a description ofprior art with respect to any claim.

BRIEF DESCRIPTION OF THE DRAWING

A full and enabling disclosure of the present disclosed embodiments,including the best mode thereof, directed to one of ordinary skill inthe art, is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 illustrates a front view of a wearable device, according to thepresent embodiments;

FIG. 2 illustrates a front view of a wearable device, according to thepresent embodiments;

FIG. 3 illustrates a front view of a head set and wearable heartratemonitor, according to the present embodiments;

FIG. 4 illustrates a front view of a toxicity detector, according to thepresent embodiments;

FIG. 5 illustrates a front view of a watch including a heartratemonitor, according to the present embodiments;

FIG. 6 illustrates a function map of a wearable device, according to thepresent embodiments;

FIG. 7 illustrates a function map of a wearable device, according to thepresent embodiments;

FIG. 8 illustrates a perspective view of a site safety and trackingsystem, according to the present embodiments;

FIG. 9 illustrates a top view of a site safety and tracking system,according to the present embodiments;

FIG. 10 illustrates a worksite safety tracking method, according to thepresent embodiments;

FIG. 11 illustrates a machine-learning ecosystem, according to thepresent embodiments; and

FIG. 12 illustrates a machine-learning ecosystem, in accordance withaspects of the present disclosed embodiments.

DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the present disclosedembodiments, one or more examples of which are illustrated in theaccompanying drawings. The detailed description uses numerical and/orletter designations to refer to features in the drawings. Like orsimilar designations in the drawings and description have been used torefer to like or similar parts of the present embodiments.

The present disclosed embodiments include apparatuses, systems, andmethods for enhancing safety and preventing accidents at worksites. Thesystem may include wearable devices, other sensors, and machine learningintegrated into a system that promotes worksite safety and performance.

Wearable Device

FIG. 1 illustrates a wearable device 10, according to aspects of thepresent disclosed embodiments. The wearable device 10 may be worn bysite personnel and workers at construction sites, job sites, oil rigs(onshore and offshore), power plants, mining areas, excavation areas,manufacturing plants, chemical processing plants, ships, disaster areas,law enforcement applications, fire-fighting applications, productionfacilities, ports, docks, airports, distribution centers, warehouses, aswell as in other areas or applications in which tracking personnel andmitigating safety risks is desirable. The wearable device 10 may includea front face 12 with one or more features disposed within it including aprogrammable logic controller (PLC) 14 which may include one or moreprogramming or control buttons 22, allowing a user to control thedevice, program the device, retrieve data from the device, and otherwisegenerally interface with the wearable device 10. The PLC 14 may alsoinclude a screen 16, one or more vertical navigation buttons 20, as wellas one or more horizontal navigation buttons 18, used for selectingvarious functions, options, and control modes of the PLC 14. In otherembodiments, the PLC 14 may include a touch screen (not shown) whichfunctions as both a display and a digital control interface that allowsa user to control and program the PLC 14, as well as to enter andretrieve data from the PLC 14. The PLC 14, screen 16, horizontalnavigation buttons 18, vertical navigation buttons 20, programmingbuttons 22, and (in some embodiments) touch screen may collectively forma local interface through which a user may locally control or programthe wearable device 10. In place of the local interface, or in additionto the local interface, the wearable device 10 may also be controlledvia one or more remote devices, according to aspects of the presentembodiments. In addition to, or in place of the PLC 14, the wearabledevice 10 may also include one or more microprocessors, as well as oneor more integrated circuits (not shown).

Referring still to FIG. 1, the wearable device 10 may also include atoxicity module 24 for sensing the presence of toxic gases andsubstances in the vicinity of the wearable device 10. The toxicitymodule 24 may include an inlet 26 allowing gases to enter the toxicitymodule 24. Within the interior of the wearable device 10, the toxicitymodule 24 may include one or more chambers (not shown) that are fluidlyconnected to the inlet 26, where toxic gases and substances may besensed via one or more toxicity sensors (not shown), disposed within theone or more chambers. The toxicity sensors may include one or more CO2sensors, LEL sensors, CO sensors, H2S (hydrogen sulfide) sensors,chlorine gas sensors, and other toxic gas sensors. LEL sensors (that is,lower exposure limit sensors) may detect and provide alerts or warningswhen a lower exposure limit to one or more combustible or toxic gaseshas been reached. The toxicity module 24 may also include one or moreoxygen sensors (not shown) for monitoring the oxygen levels in thevicinity and sending an alert if the local oxygen level drops or risesto an unsafe level (for example, less than 19.5 percent or more than 22percent), as well as one or more hydrocarbon sensors. The toxicitymodule 24 may also include one or more moisture sensors (not shown)disposed internally within the wearable device 10 for detecting ifmoisture is present on or around the one or more toxicity sensors, so asto provide an indication of whether the toxicity sensors may beinoperable or possibly malfunctioning. The one or more moisture sensorsmay be disposed in the immediate vicinity of the one or more toxicitysensors such that any moisture detected by the moisture sensor has ahigh likelihood of indicating that moisture has also been disposed onthe one or more toxicity sensors. Stated otherwise, the one or moremoisture sensors may be disposed in the interior of the wearable device10, within the one or more chambers, downstream of and fluidly connectedto the inlet 26.

Referring still to FIG. 1, the toxicity module 24 may also include afilter (not shown) disposed downstream of the inlet 26, but upstream ofthe chamber, in order to prevent dirt, food scraps, debris, and othersubstances from clogging the inlet 26. An inlet guard 28 may be disposeacross the front of the toxicity module inlet 26 in order to prevent thetoxicity module 24 from becoming blocked, which may result in the one ormore toxicity sensors becoming fluidly disconnected from the exterior ofthe wearable device 10. The inlet guard 28 may include a smallerdiameter or width than the inlet 26, allowing gas to enter the toxicitymodule both below and above the inlet guard 26. In the embodimentillustrated in FIG. 1, the inlet guard 28 and inlet 26 extendhorizontally (or laterally) across the wearable device 10. In otherembodiments, the inlet guard 28 and inlet 26 may extend vertically (orlongitudinally) across the wearable device 10. In other embodiments, thewearable device 10 may have multiple toxicity modules 24, and the inletguard 28 and inlet 26 may both be square, circular, elliptical,rectangular, triangular, as well as other suitable shapes. The toxicitysensor may utilize spectrographic gas detection (that is, aspectrometer), infrared detection, chromatography, as well as othermethods. The toxicity module 24 may include one or more infraredcollection mirrors, lenses, collimators, and other optical components.

Still referring to FIG. 1, the wearable device 10 may include a speaker30 for playing audible alerts and for use in verbal communications withother devices, as well as a microphone 32 for verbal communications withother devices and for monitoring ambient noise levels, to ensure sitepersonnel are not exposed to excessively noisy conditions. Themicrophone 32 may also be used for accepting voice commands from thewearer of the wearable device 10, as well as for one or more voice orspeech recognition functions of the wearable device 10. For example, thewearer of the wearable device 10 may give a verbal command to send animage taken by a camera to a network. In another example, the wearer ofthe wearable device 10 may provide a verbal description of an imagetaken by the camera (for example, “detail of damaged pump”) or video(for example, “video of ongoing water leak”) that gets transposed intotext by device or network-based speech-recognition software, and issubsequently transmitted to the network as metadata, along with theimage or video.

Still referring to FIG. 1, the wearable device 10 may also include anantenna 34 disposed at the top of the wearable device 10 or at anothersuitable location, as well as a vibrating tool 36 for alerting theworker wearing the wearable device 10 of a potentially hazardouscondition. The vibrating tool 36 may be of particular use when otheraudio alerts are inaudible due to ambient noise levels, or in caseswhere the worker is wearing earplugs, head phones, or other noiseblocking devices. The wearable device 10 may also include both atemperature sensor 38, and a relative humidity sensor 40, for measuringambient conditions in the vicinity of the wearable device 10. Using boththe temperature sensor 38 and the relative humidity sensor 40, a dewpoint temperature may be calculated by the wearable device 10, which maybe a better indicator of the strenuousness of the ambient conditions,than either temperature of relative humidity alone. For example,exposure to dew point temperatures above about 70 degrees Fahrenheit (orabout 21.1 degrees Celsius) for excessive periods of time may lead toexhaust, heat stroke, and even death. The wearable device 10 may alsoinclude a heartrate monitor 42 including one or more electrodes or othercomponents physically or communicatively coupled to the pulse orheartbeat of the worker. The heartrate monitor 42 may be used to ensurethat the worker's heart is beating and also to ensure the worker is notexperiencing an excessively high heartrate, an excessively lowheartrate, a heart attack, heart palpitations or other unhealthyconditions.

Referring still to FIG. 1, the wearable device may include one or morecameras 44 disposed in the front face 12 for providing both videomonitoring and image-capture capabilities. The video and image-capturefunctions of the one or more cameras 44 may be activated via one or moreactivation buttons 45, as well as via one or more buttons disposed onthe PLC 14. In other embodiments, the video and image-capture functionsof the one or more cameras 44 may be activated remotely via a network orremove device that transmits a picture or video request to the wearabledevice 10. The wearable device 10 may then capture the video or imagevia the one or more cameras 44 and transmit it back to the network orremote device. In one embodiment, the remote network or device maytransmit a signal to the wearable device 10 to capture a video or imageof a potential damage or hazard area, which then may be transmitted backto the remote network or device for further analysis and potentialfollow-on action.

Still referring to FIG. 1, the wearable device 10 may also include acommunication module 46 for allowing the wearable device 10 tocommunicate with other devices or remote networks. The communicationmodule 46 may include one or more receivers for receiving data andsignals, as well as one or more transmitters for transmitting signalsand data. In addition, the communication module 46 may include one ormore transceivers capable of both transmitting and receiving signals anddata to and from remote devices and networks. The communication module46 may include transceivers (or receivers and transmitters) withinternal circuitry that operates at multiple frequency ranges. In oneembodiment, the communication module 46 may include transceivers thatoperate at two different frequency ranges (that is, “dual-band”transceivers). In another embodiment, the communication module 46 mayinclude transceivers that operate at three different frequency ranges(that is, “tri-band” transceivers). In another embodiment, thecommunication module 46 may include transceivers that operate at fourdifferent frequency ranges (that is, “quad-band” transceivers). Inanother embodiment, the communication module 46 may include transceiversthat operate at more than four different frequency ranges.

Referring still to FIG. 1, the communication module 46 may include oneor more Bluetooth transceivers operating in a frequency range from about2400 MHz to about 2484 MHz. The communication module 46 may also includeone or more wireless (that is Wi-Fi) transceivers operating in afrequency range from about 2.3 GHz to about 2.5 GHz, or from about 4.9GHz to about 5.1 GHz. The communication module 46 may also include oneor more cellular transceivers operating in a frequency range from about800 MHz to about 900 MHz, or from about 825 MHz to about 849 MHz, orfrom about 869 MHz to about 894 MHz. The communication module 46 mayalso include one or more cellular transceivers operating in a frequencyrange from about 1800 MHz to about 1900 MHz. The communication module 46may also include one or more Global Positioning System (GPS)transceivers operating in a frequency of about 1575 MHz (+/−10 MHz) orat about 1227 MHz (+/−10 MHz). The communication module 46 may alsoinclude one or more enhanced specialized mobile radio (ESMR)transceivers operating in a frequency range from about 862 MHz to about869 MHz. The communication module 46 may also include one or moretwo-way radio transceivers operating in a frequency range from about 851MHz to about 862 MHz, or in a frequency range from about 136 MHz toabout 850 MHz, or in a frequency range from about 100 MHz to about 900MHz.

Still referring to FIG. 1, the wearable device 10 may include one ormore communication links or couplings 54 to other devices such as otherportable devices 58 (including other wearable devices 10, as well ascell phones, smart phones, tablets, laptop computers, radios, pagers,and other devices), as well as network computers 56 and servers. Thewearable device 10 may also include a charging port 50 into which awired charger may be plugged, as well as an internal battery which maybe charged via wired charger or alternatively may be charged wirelessly.The wearable device 10 may also include one or more beveled, rounded orchamfered corners 60, as well as one or more accelerometer 52. The oneor more accelerometer 52 may include one or more gyroscopes (not shown)for tracking an angular moment of the wearable device 10 in order todetermine when the wearable device is oriented in an upright position,an upside down position, a sideways position, a diagonal position, aswell as a face-down position. The accelerometer 52 may also be used todetermine if the site personnel wearing the wearable device 10 hastripped or fallen, due to the accelerometer 52 sensing excessiveaccelerations (for example, due to a sudden impact with the ground).

Referring still to FIG. 1, the wearable device 10 may include a storagemodule 43 including additional memory such as random access memory(RAM), read-only memory (ROM), programmable read only memory (PROM),erasable programmable read only memory (EPROM), electrically erasableprogrammable read only memory (EEPROM), flash memory, volatile memory,non-volatile memory (NVM), cache memory, buffer memory, and other typesof memory. The amount of storage that may typically be available withina PLC may range from about 96 kilobytes (KB) to about 1024 KB or otheramounts which may be insufficient for recording multiple parameters (forexample heartbeat, temperature, humidity, acceleration, ambient noise,and others) over extended periods of time. The storage module 43, whichmay be external to the PLC 14 but internal to the wearable device 10,may be used for storing the various sensor data and associatedparameters at a wide-range of frequencies (for example, at intervals asfast as one or two milliseconds), and for a wide-range of durations(minutes, hours, days, weeks, months, or even years, depending on theapplications and the exact suite of parameters being tracked, collected,and recorded). The storage module 43 may include a memory capacityranging from one or two megabytes (MB) to several megabytes, to one ortwo gigabytes (GB), up to several gigabytes, and even up to one or twoterabytes (TB). The storage module 43 may also include one or moremicroprocessors, or integrated circuits, or both one or moremicroprocessors and integrated circuits, separate from those that may beincluded in, or associated with, the PLC 14.

Still referring to FIG. 1, the wearable device 10, in connection withthe storage module 43, may also include structures allowing forremovable storage. For example, the storage module 43 may include auniversal serial bus (USB) port 47 or interface allowing a USBthumb-drive or flash drive to be plugged into the wearable device 10.The USB thumb-drive or flash drive may remain inserted while thewearable device 10 is in operation at a worksite, allowing data to bedirectly stored on the USB thumb-drive or flash drive. In otherembodiments, operational data and parameters may be stored in theinternal memory of the wearable device 10 (for example in the storagemodule 43), and then downloaded, copied, or removed from the storagemodule 43 via the USB port 47 and copied or moved to the flash drive orthumb drive. Similarly, the storage module 43 may also include one ormore SD card access ports 49, as well as, one or more SD mini (or SDmicro) card access ports 51. The USB port 47 as well as the SD cardaccess port 49 and the SD mini (or SD micro) card access port 51 allowremovable storage to be inserted directly into the wearable device 10.The removable storage may range from less than a MB, to 8 MB, 16 MB, 160MB, 1 GB, 2 GB, 16 GB, 32 GB, and even as high as one or two TB. The USBport 47 may operate at a power range from about 2.5 W to about 30 W, ata voltage range of about 5 volts (+/−3 volts), and at a current rangefrom about 100 mA to about 3000 mA.

Referring still to FIG. 1, the wearable device 10 may transmit data toone or more remote devices 58, networks 56, or to both remote devices 58and one or more networks 56 via several different processes orprotocols. In one embodiment, the wearable device 10 may transmit datain a near real-time fashion where every one (1) to five (5) or ten (10)seconds, newly recorded data is transmitted to one or more networks 56or other device, where it can be placed in long-term storage, trended,and analyzed. In other embodiments, the data may be transmitted at morefrequent intervals than every second, or at less frequent intervals thanonce every 10 seconds, or at other intervals between one (1) and ten(10) seconds. In other embodiments, once every 15 minutes, the data thathas been recorded and logged in the storage module 43 or in removablestorage devices may be compressed at a compression module 53, such thatthe size of the data file or files to be transmitted is minimized, priorto transmission, which may occur at fifteen (15) minute intervals. Inother embodiments, data may be stored on the wearable device 10 (forexample, within the storage module 43) for an entire day, and then allof the data from a given day may be compressed (for example, within thecompression module 53) and then transmitted in a single transmission atthe end of the day. In other embodiments, data may be stored on thewearable device 10 for other intervals such as 3, 5, 7, 10, 15, 20, 30days or longer between data transmissions to the network 56. In otherembodiments, the data may only be transmitted from the wearable device10 via removable devices (for example, rather than being wirelesslytransmitted via the communications module 46).

Still referring to FIG. 1, the wearable device 10 may transmit data tothe server 56 according to a condition-based system. In one embodiment,the wearable device 10 may not transmit data to the server 56 unless acondition is met. The condition may include alerts, or out of rangesensed parameters including excessive ambient temperatures, excessivedecibel levels, toxic gases sensed by the toxicity module 24, an erraticheartbeat (or stopped heartbeat) sensed by the heartrate monitor 42, 78,as well as other conditions, which may be defined by the user. In otherembodiments, hybrid data transmission protocols may be employed wheretwo or more rules are applied dictating the transmission of data fromthe wearable device 10 to the network 56. For example, the wearabledevice 10 may transmit data to the network every hour, as well as when arequest for data by the wearer of the wearable device 10, the network,or another party (such as a system supervisor or another device 58) ismade. In another example of a hybrid data transmission protocol, thewearable device 10 may transmit data to the server 56 once per day, butalso when the internal storage module 43 or buffer is getting full. Inanother example of a hybrid data transmission protocol, the wearabledevice 10 may transmit certain parameters (for example heartrate, gasdetection (or lack thereof) and worker location) to the server 56 on areal-time or near real-time basis while transmitting other parameters(such as temperature, humidity, and ambient noise levels) on a much lessfrequent basis (for example once per day or once per hour). In anotherexample, the wearable device 10 may transmit only a partial dataset (forexample, important or time-sensitive parameters) to the server whileremoving or sifting out other parameters or data time periods to betransferred at a later time. By selectively transmitting data only atcertain times, at certain intervals, under certain conditions, ortransmitting only certain parameters, the wearable device 10 mayminimize overheating, central processing unit (CPU) usage, and repeatedcalls to the server 56, while simultaneously maximizing battery life,enhancing CPU performance, and avoiding unnecessarily saturating theairways with transmission signals within certain frequency bands widths.

Each of the components of the wearable device 10 (for example, the PLC14 (and PLC components), the toxicity module 24, the speaker 30, themicrophone 32, the antenna 34, the vibrating tool 36, the temperaturesensor 38, the humidity sensor 40, the heartrate monitor 42, the storagemodule 43, the camera 44, the communication module 46, the accelerometer52, and the compression module 53) may be coupled communicatively orelectrically or both communicatively and electrically via a firstelectronic coupling 48, which may be disposed in one or more printedcircuit boards (PCB; not shown) or other internal electrical circuitrydisposed within the interior of the wearable device 10. In otherembodiments, one or more of the components of the wearable device 10 maybe communicatively coupled to at least one other component of thewearable device 10 via at least one wireless coupling. In one or moreembodiments, the screen 16 of the PLC 14 may become illuminated in asingle color (for example, but not limited to, green, yellow, or red) ifthe wearable device 10 has been exposed to toxic substances at levelsthat exceed predetermined thresholds. Similarly, in one or moreembodiments, the screen 16 of the PLC 14 may begin to flash if toxicsubstances have been detected by the toxicity module 24. By illuminatingthe screen 16 in bright lights, flashing lights, or both bright lightsand flashing lights, the wearable device 10 may allow both the wearer ofthe wearable device 10, as well as others in the immediate area tobecome aware that toxic substances may be present. The wearable device10 may include several arrangements such that each of the components andfeatures illustrated in FIG. 1 may be omitted from the wearable device10, of may be arranged in different locations and orientations than whatis illustrated. The wearable device may also include other components,sub-components, and features, in addition to what is shown in FIG. 1.

FIG. 2 illustrates an embodiment of the wearable device 10 according toaspects of the present disclosed embodiments. In the embodiment of FIG.2, the wearable device may include a smart device 62 (such as a phone,tablet, or other electronic device) coupled via a universal serial bus(USB) connector 66 to an external toxicity detector 64. The smart device62 may include a touch screen 70, a home button 72, as well as one ormore software applications 68 for interfacing with the external toxicitydetector 64 and for performing the functions of the wearable device 10.The smart device 62 may also include or house a PLC 14, a speaker 30, amicrophone 32, an antenna 34, a vibrating tool 36, a storage module 43,a camera 44, a communication module 46, an accelerometer 52, and acompression module 53. The external toxicity detector 64 may include atoxicity module 24 including an inlet 26 for fluidly connecting theinterior of the toxicity detector 64 to the exterior of the externaltoxicity detector 64, as well as an inlet guard 28 for preventing theinlet 26 from getting blocked or clogged. The external toxicity detector64 may also include a temperature sensor 76 as well as a humidity sensor74 for measuring the respective temperature and humidity in the vicinityof the external toxicity device 64. The external toxicity detector 64may also include an internal battery (not shown) that is chargeable viathe USB connector 66. The external toxicity device 64 may alsocommunicate with the smart device 62 via the USB connector 66. The smartdevice 62 may also include a heartrate monitor (not shown).

FIG. 3 illustrates an exemplary external heartrate monitor 78 accordingto an embodiment of the present disclosure that may be disposed on aworker thereby sensing the heartbeat of the worker, and may becommunicatively coupled to the wearable device 10, in embodiments wherethe wearable device 10 does not include at least one heartrate monitor42, 78. The heartrate monitor 78 may be strapped to the chest or otherbody of the worker via one or more straps 81, such that the heartratemonitor 78 is connected in the vicinity of a pulse of the worker. In theembodiment of a FIG. 3, the wearable device 10 may include an externalheadset 79 that is used to both block out ambient noise, and deliveraudible messages, alerts, and other sounds directly to the eardrums ofthe worker. In some embodiments, the external headset 79 may include earplugs that can both block out noise and act as microphones or speakers.The external headset 79 may communicate with the wearable device via oneor more wired or wireless connections. The external headset 79, with oneor more embedded speakers, may also be integrated into a hard hat orhelmet that site personnel may already be required to wear, and could beparticularly beneficial in noisy areas such as on an oil rig derrick, inthe standing on or in the vicinity of a drill site monkey boards, withinconfined space locations, in the vicinity of a turbine deck, boiler, orgenerator at power plants, as well as at construction and manufacturingsites where machinery is being operated. As such, the external headset79 may be used both to position speakers close to the eardrums of theworker, and also to at least partially block ambient noise.

FIG. 4 illustrates an external toxicity detector 64 according to thepresent embodiments including a temperature sensor 76, a humidity sensor74, a toxicity module 24, an inlet 26, and an inlet guard 28. In theembodiment of FIG. 4, the external toxicity detector 64 may include aclip, hook or handle 82 for connecting the external toxicity detector 64to at least one worker, as well as a charging port 80 for electricallycharging an internal battery (not shown) or for communicating with theexternal toxicity detector 64. In the embodiment of FIG. 4, the externaltoxicity detector 64 may further include at least one transceiver (notshown) for wirelessly communicating with other devices and networks.

FIG. 5 illustrates a wearable device 10 according to aspects of thepresent embodiments. In the embodiment of FIG. 5, the wearable device 10may include a watch that includes a heartrate monitor 42, a touchscreen/display 86, and one or more buttons 84 for controlling thewearable device 10. The watch 10 may include the other sensors andfunctions of the wearable device 10 as illustrated in FIG. 1. In otherembodiments, the watch 10 may communicate with another wearable device10 and may include at least one transceiver for wirelessly communicatingthe heartrate to the wearable device 10, and for receiving one or moresignals from the other wearable device 10.

FIG. 6 illustrates a function map of the wearable device 10 according toaspects of the present embodiments. The function map illustrates one ormore examples of how the wearable device may function internally, andinteract with one or more networks 56, as well as with other devices 58.According to aspects of the present embodiments, local alerts 100 may begenerated on the wearable device 10, which then may be converted at step102 into local actions 104. The local alerts 100, or the local actions104, or the local alerts 102 and the local actions 104 may then betransmitted via one or more signals at step 106 to one or more networks56. The signals may be received by a decision module 108 within thenetwork 56, which may then transmit one or more communications at step110 back to the wearable device 10. At step 114, the wearable device 10may process the one or more communications from the network 56 and atstep 118, send follow-up communications, signals, and data back to thenetwork 56. The network 56 may include an automated actions module 120where one or more actions may be taken within the network 56, withoutrequiring direction or signaling from the decision module 108. Thenetwork 56 may include a site-wide hazards module 122 where systemalerts 126 may be generated and distributed at step 124 to remotedevices 58 including the wearable device 10, as well as second, third,fourth, and higher number devices 58. In some embodiments, the wearabledevice 10 may send system alerts directly to other devices 58, withoutrequiring any intervention by the network 56.

FIG. 7 illustrates a function map of the wearable device 10 according toaspects of the present embodiments. In the embodiment of FIG. 7, localalerts 100 that are generated on the wearable device 10 may include oneor more of: a toxicity exceedance, a temperature being out of range (orhigher than a predetermined threshold), a heartrate being stopped (forexample, the wearer of the wearable device 10 is experiencing a heartattack), a decibel exceedance in the local vicinity, a moisture alarm orfault (indicating that the toxicity module may be inoperable orpotentially malfunctioning), a movement fault (indicating that thewearer of the wearable device 10 may be injured or unconscious, asdetermined by the decision module 108 from a lack of movement of thewearable device 10), and an oxygen fault (indicating that local oxygenlevels may be too high or too low). Potentially resulting from, andpotentially following the local alerts 102, local actions 104 which mayoccur on or within the wearable device 10, and may include displaying atext message on the screen 16, initiating a local vibration via thevibration tool 36 to alert the wearer of the wearable device 10,illuminating the screen 16 with flashing or bright lights, as well asactivating a local audible alarm via the speakers 30. The wearabledevice 10 may then send an alert, at step 128, to the network 56 whichmay be received at the decision module 108. The decision module 108 maythen take one or more actions at step 130, including pinging thewearable device 10 to assess if the wearer of the wearable device 10 isokay or, for example, to assess if the alert may have been a falsealarm.

Referring still to FIG. 7, in one embodiment, the wearer of the wearabledevice 10 may confirm at step 132 via one or more of the buttons 18, 20,22 that the alert that was sent to the network 56 was in fact real (oralternatively that it was false), which then may cause the network 56 totake a follow-on action such as requesting images at step 134, orsending a site-wide alert. Stated otherwise, at step 134, one or morelocal actions may be initiated upon receiving a signal at thecommunications module 46 of the wearable device 10, from the network 56.At step 136, the body cam or camera 44 disposed on the wearable device10 may be activated, and may capture one or more videos or images atstep 138, which may then be transmitted back to the network 56 at step140. The images may also be transmitted, at step 142, directly back tothe decision module 108, where additional analysis or evaluation mayoccur. At the automated actions module 120, the network may take one ormore automated actions such as updating a database with the receivedimages or other information, without requiring instructions from thedecision module 108 to do so. The hazards module 122 may includeindications of emergency conditions received directly from the wearabledevice 10 or from other devices 58. Indications of emergency conditionsmay also be received at the hazard module from the decision module basedon inputs from the wearable device 10 or other devices 58. The hazardsmodule 122 may also generate a hazard based on a location fault as aresult of the wearer of the wearer device 10 entering a zone that hasbeen temporarily or permanently tagged as a danger zone.

The wearable device 10 may be used in a system that includes wirelesssite-mapping devices, controls, display panels, as well as network ordevice-based algorithms for performing smart analyses. The wearabledevice 10 may interact directly with site-mapping devices, as well asother components of the system. In one embodiment, the system mayanalyze and evaluate health risks to an individual and trigger an actionto stop a job, confirm an alarm, or take a required action, asnecessary. In another embodiment, the system may trigger a safetynotification if there has been a gas release, so that other parties inthe area are aware of the gas release and can evacuate the area ifrequired. By placing sensors such as heartrate monitors 42, toxicitydetectors 24 (or gas detectors), temperature sensors 38, microphones 32,and other sensors in a wearable device 10 that is worn by site workers,site personnel can immediately be notified of potential hazards, therebyreducing or eliminating many injuries, and allowing the system to recordand assess hazards for future avoidance.

The wearable device 10 may also be used to track human health andperformance. For example, in one embodiment, the wearable device 10 maylog (or transmit to the network 56 for logging) heartrate informationfor an individual (that is, recorded by the heartrate monitor 42), whichmay be trended over time to track long-term health trends. In anotherembodiment, the wearable device 10 may track the overall level ofactivity of an individual using the GPS function of the communicationmodule 46, using the accelerometer 52, or using both the GPS functionand the accelerometer 52. For example, the network 56 may log an overallactivity level or equivalent “steps” that an individual has taken in agiven day, based on data received from the wearable device 10 sensed bythe GPS or by the accelerometer 52. The number of steps can then betrended over time, or can be compared to baseline levels of activity, topredict the overall health status of an individual, to recommendmitigating actions to improve the health of the individual, or topredict the productivity level of the worker.

In one embodiment the wearable device 10 may aid in personnel trackingduring safety drills, or in locating individuals during emergencies.Each wearable device 10 may include a unique identifier associated onlywith the individual who is wearing the wearable device 10. As such, byautomatically tracking each wearable device 10 via the unique identifierand the GPS function of the communications module 46, the network 56 mayobtain a real-time status of the precise location of each and everyindividual, and will thus be cognizant of any individual(s) who is/arenot at the emergency assembly area (and who might be in a potentiallydangerous zone of the work site). Tracking site personnel in emergencysituations using the wearable device 10 and network 56 may have theadded benefit of not requiring any intervention on the part of theindividual or worker. Stated otherwise, using the wearable device 10 andnetwork 56 alleviates the worker from having to pause in an emergencysituation to insert a T-card into a slot, or from having to take anotheraction in order to communicate his or her location to a network orsupervisor. The network 56 may also use information from all of thewearable devices 10 deployed in and around a worksite to redirectpersonnel and workers (in a real-time or near real-time fashion) toalternate locations if the normal rally point or assembly area is withina danger zone.

Site Safety Tracking System

In one or more aspects, the present disclosure describes a site safetytracking (SST) system that aids in tracking crew and personnel atworksites including those in the oil and gas, power, construction,shipping, manufacturing, production, distribution, transportation,chemical processing, and refining industries, as well as in otherindustries. The SST system facilitates and enhances the collection ofworker, contractor, and site personnel-related data which includesincidents, injuries, inspection findings, observations, investigations,recommendations, contractor and employee training history, riskmanagement, site condition and status (including the condition ofequipment and temporary or permanent structures), accident reports, andother relevant safety information. The SST system may include one ormore networks for receiving, recording, characterizing, and analyzinguser inputs as well as inputs received from wearable devices 10 andwireless site mapping devices which are placed across the job site orworksite and used to map the site into grids.

FIG. 8 illustrates a perspective view of a site safety and trackingsystem 200 installed at a worksite in the oil and gas industry. Theworksite may include one or more drill platforms 202 connected to arotary table 204 for rotating a drill pipe or tubular 206 in a top-drivesystem (TDS) including a derrick 208 and a crown block 210. The worksitemay also include one or more mud pumps 214 fluidly connected to aborehole (not shown) or drill string via one or more mud discharge lines216, as well as with one or more water tanks 218 and one or more fueltanks 220. The fuel tanks 220 may be used to fuel one or more engines(not shown) in a power house or engine house 222 used for electricallypowering the various equipment at the worksite or job site. The one ormore water tanks 218 may be used to make mud and supply water to thesite (among other functions) and may be fluidly connected to one or moremud pits 228, or alternatively, may be fluidly connected to other siteequipment or components (such as a mud house or other pipes and pumps)that are connected to the mud pit or reserve pit 228. The site may alsoinclude one or more degassers 232 connected to the mud pit or reservepit 228 via one or more degas lines 236. The site may also include amud-gas separator 230 and one or more choke manifolds 234 fluidlyconnected to each other, as well as a staging area 212 where drill pipesand tubulars may be stored in the vicinity of the drill platform 202.The site may also include a parking lot where one or more tanker trucks226 as well as passenger vehicles, delivery vehicles, and equipmenttrucks may park proximate the work site. One or more buildings 224,which may include one or more trailers, containers, as well as morepermanent building structures, may be used as (or may include) a controlroom, a command center, an office, a kitchen area, a meeting area, allof the preceding, or any subset thereof. The worksite may include one ormore other components not shown in FIG. 8 including (but not limited to)a mud house, storage shed, warehouses, desilters, desanders, shaleshakers, mud and water return lines, pipe racks, pipe ramps, catwalks,wiring and piping trestles or conduits, accumulator units, platformbased doghouses, control consoles, gauges, indicators, winches (ordrawworks), hocks, swivels, traveling blocks, lubricators, boreholeequipment, boost pumps, and other worksite components.

FIG. 9 illustrates a top view of the site safety and tracking system 200according to aspects of the present disclosed embodiments including thedrill platform 202, the crown block 210, the staging area 212, the mudpumps 214, the mud discharge lines 216, the water tanks 218, the fueltanks 220, the engine house 222, the personnel buildings 224, the tankertrunk 226, the reserve pit 228, the mud-gas separator 230, the degassers232, the choke manifolds 234, and the degas lines 236. In the embodimentof FIG. 9, the worksite has been mapped into six (6) different zones. Afirst zone 238 may include the reserve pit 228. A second zone 242includes the water tanks 218, the degassers 232, and the mud-gasseparator 230, as well as potentially other components of a drillingfluid circulation system. A third zone 244 may include the chokemanifolds 234, mud pumps 214, and mud discharge lines 216. A fourth zone246 may include the drill platform 202, the crown block 210, the stagingarea 212, as well as other components of the top-drive drilling system.A fifth zone 248 may include the personnel buildings 224 and the parkinglot including the tanker truck 226 and other vehicles parked there. Asixth zone 250 may include the engine house 222 and fuel storage tanks220, as well as other components of the worksite power system. Each ofthe first, second, third, fourth, fifth, and sixth zones 238, 242, 244,246, 248, 250 may be defined by one or more boundary markers 240defining the geometric borders of each zone. The zones defined asillustrated in FIG. 9 are for exemplary purposed. The present disclosedembodiments may include other zone configurations based on the layout ofthe site, as well as the specific equipment and systems on site.

Referring still to FIG. 9, the SST system 200 may also include one ormore wireless routers 252 located throughout the worksite at centrallocations to allow for strong signal transmissions between devices. Eachboundary marker 240 map include a transceiver (or receiver andtransmitter) operable on a GPS frequency (for example about 1575 MHz,+/−10 MHz or about 1227 MHz, +/−10 MHz) as well as on a Wi-Fi frequency(for example about 2.4 GHz, +/−10 MHz or 5 GHz, +/−10 MHz) such thatthey can communicate both with GPS satellites as well as with site Wi-Finetworks 252 and other devices. For example, using signal transmissionsback and forth with GPS satellites, the boundary markers 240 canestablish the GPS coordinates that define each of the boundary verticesor corners. For example, each of the first, second, fourth, fifth, andsixth zones 238, 242, 246, 248, 250 are square or rectangular andtherefore may be defined using four boundary markers 240. By contrast,the third zone 244 is more L-shaped and thus requires 6 boundary markers240 to define its borders. Each zone may be defined using any suitableshape including triangular, rectangular, square, pentagonal, hexagonal,octagonal, and even circular.

Still referring to FIG. 9, in some embodiments, the zones may be definedby boundary markers 240 that can detect radio frequency identification(RFID) tags 254 (coupled to the tanker truck 226 in FIG. 9) associatedwith or coupled to one or more electronic devices (including wearabledevices 10 and other devices 58), as well as plant equipment andvehicles. In the embodiments that use RFID tags 254, the boundarymarkers 240 may be operable within one or more radio frequencybandwidths including from about 125 kHz to about 134 kHz, as well asabout 13.56 MHz (+/−0.1 MHz). In these embodiments, the boundary markers240 (or RFID receivers) may be operable within three (3) or morefrequency bands (for example, frequency bands corresponding to GPS,Wi-Fi, and radio frequency, as previously discussed). In otherembodiments, the zones may be defined by proximity to the nearestboundary marker 240, rather than by mapping out the edges of each zone.For example, RFID tags 254 have a range from about 10 feet to about 600feet, depending on the type. As such, a grid of RFID receivers may beset up at a worksite such that the spacing between RFID receivers (orboundary markers 24), as well as the range of the RFIDs 254 being usedresults in one (1), two (2), three (3), or at most only four (4) (orsome other finite number of) RFID receivers 240 being in communicationwith a given RFID tag 254, therefore allowing the approximate locationof the given RFID tag 254 to be determined.

Referring still to FIG. 9, each of the boundary markers 240 may bebattery powered and may be moveable such that using the internal GPStransceivers, the zones will automatically be re-mapped by the SSTsystem 200, thereby allowing the SST system 200 to update each zoneaccording to the new boundary marker 240 locations. When a workerwearing a wearable device 10 is walking through the worksite, the SSTsystem 200 will instantly track which zone the worker is in, based onthe GPS location transmitted to the network 56 via the wearable device10. Being able to move the boundaries of the zones, or to set up newtemporary or permanent zones allows the SST system 200 to be adaptive tothe safety and operational needs of the worksite and personnel thereof.Even in situations where no physical change has occurred at theworksite, additional or re-mapped zones may be desired if newinformation has become available regarding the nature of a particularrisk or hazard. In other embodiments, “virtual zones” may be establishedby simply defining the GPS coordinates corresponding to the boundariesof the various zones, without the need to continually use boundarymarkers 240. Using virtual zones, the SST system 200 may computationallydetermine if a given wearable device 10 falls within a zone based on theGPS coordinates that define the zone, as well as the GPS coordinatescorresponding to the location of the wearable device 10. The SST 200 mayalso include 3-D zones that use altimeter or GPS data to define thevertical dimension of a zone. 3-D mapping of zones may be beneficial inapplications where the lateral and longitudinal boundaries are differentfrom one vertical level to the next (for example on ships with multipledecks, on oil rigs (both on-shore and off-shore) as well as powerplants, and construction sites (for example sky-scrapers) with multiplelevels. For example, at a level of a rig platform 202 or rig floor,hazards may include a swinging crane, falling off the rig platform 202,the rotary table 204, as well as other hazards, while at the level belowthe rig platform 202 (that is, at ground level under the rig platform202), but at similar longitudinal and latitudinal coordinates, thehazards may include only a rotating drill string 206.

Still referring to FIG. 9, zones similar to those illustrated for an oiland gas industrial application may be established in other applicationsand industries. For example, in a combined-cycle power plant setting, afirst zone may include a gas turbine, generator, inlet filter house andexhaust duct. A second zone may include a heat recovery steam generator(HRSG). A third zone may include a steam turbine, turbine deck,generator, condenser, heaters, and associated piping. A fourth zone mayinclude a high-voltage yard or switch yard. A fifth zone may include agas or fuel processing area and associate equipment. Finally, a sixthzone may include a control room, offices, and other site personnelareas. In another example applying to warehouse applications, zones mayinclude staging, docking, receiving, long term storage, active packing,and office zones. In each application, the number and size of the zonesmay be determined such that they are usefully defined with respect tothe specific hazards and risks of the particular application, as well asthe site layout or arrangement of components and equipment at the site.

FIG. 10 illustrates a worksite safety tracking method 300. At step 302,the method 300 may include receiving data at the network 56. The datamay include data transmitted from one or more wearable devices 10, datafrom boundary markers 240, data from other plant or worksiteinstrumentation, as well as user input data such as informationregarding the status of an ongoing project that may present particularrisks or hazards. For example, in a construction site example where askyscraper is being built and changing on a daily basis, the safetyhazards may change from one level to the next. For example, on a givenday or plurality of days, a level or story of the skyscraper may beginto be constructed with no walls, stairs, or other safety features. Thestory may then include walls, stairs, railings, and other safetyfeatures preventing workers from risking deadly falls. As such, as theconstruction project progresses, users can update the latest hazard andsafety data such that the SST system 200 is enabled to alert the workerswith updated risk factors, which may be changing on a day-by-day, oreven hour-by-hour basis. At step 304, the method 300 may includedecompressing data that was compressed prior to transmission to thenetwork 56. At step 306, the method 300 may include parsing the data,which may include understanding the content and structure of the datathat was included in the data input. For example, different datatransmissions include different numbers of parameters, data points,inputs, outputs, and associated metadata. By parsing the data,appropriate data structures may be set up that adjust the correct numberof independent and dependent parameters according to what exists withinthe data input. At step 308, the method 300 may include collating thedata to put it in the right format for logging in one or more databases,and for tagging metadata and other various attributes of the input data.At step 310, the method 300 may include filtering the data to removenull values as well as faulty data (such as an ambient temperature of−99999, or a relative humidity of 6000%). At step 312, the method 300may include characterizing the data into one or more categories such asaccident data, normal operating data, personal health data, zoneboundary data, as well as other types of data.

Referring still to FIG. 10, at step 314, the method 300 may includelogging the data (that is, after it has been parsed, collated, filtered,and characterized) in one or more databases. At step 316, the method 300may include updating a neural network, correlation matrix, or machinelearning protocol to include new correlations or connections that resultfrom the data that was received at the network at step 302. At step 318,the method 300 may include sending the data to a decision module 108 foranalysis. The decision module 108 may recommend, at step 320, that asite-wide alert be sent out (for display on wearable devices 10, butalso on site monitors and displays, as well as broadcast over publicannouncement (PA) systems) indicating that a gas leak has been detectedin one or more zones, and to evacuate the area. The decision module 108may also recommend, at step 320, that one or more safety or repair crewsbe dispatched to the site of the gas leak with appropriate personalprotection devices and equipment, in order to mitigate the leak. At step322, the method 300 may include confirming, at a site supervisor 422(shown in FIG. 11), one or more recommendations made by the decisionmodule 108. In some embodiments, intervention (either confirming arecommendation or overriding a recommendation) may be required by a sitesupervisor. In other embodiments, the SST system 200 may have theauthority to act directly on recommendations made by the decision module108.

Still referring to FIG. 10, at step 324, the method 300 may includeinitiating one or more actions that were recommended by the decisionmodule at step 320 and confirmed at step 322 (for example, dispatchingsite personnel to an accident area, or sending a site-wide evacuationalert). In some embodiments, one or more of steps 302-324 may berepeated. In other embodiments, one or more of steps 302-324 may beomitted. In other embodiments, one or more steps may be performed in adifferent order than what is illustrated in FIG. 10. For example,updating a neural network 316 or correlation matrix may occur at anytime during the process that a new correlation or connection occurs. Inother embodiments, additional steps to what is shown in FIG. 10 may alsobe included. For example, after decompressed, parsed, collated, andfiltered data inputs are received at the decision module 108, thedecision module 108 may ping a wearable device 10 that has ceasedsending data to initiate a response from the wearable device 10 in orderto assess if the device is operational. In another example, the decisionmodule 108 may request images, videos, or other data from the wearabledevice 10 (for example sound recordings so the decision module 108 canassess an audio signature of an event), or specific data from plant orfacility instrumentation.

In operation, the site safety and tracking (SST) system 200 may detectall personnel wearing the wearable devices 10 such that their respectivelocations are known on a real-time or near real-time basis. For example,other parameters may be recorded and stored locally on the wearabledevice 10 and intermittently transmitted or uploaded to the network 56,while GPS or location data is tracked and transmitted to the network 56on a second-by-second basis. The SST system 200 may then map out (on areal-time or near real-time basis) which zone each worker is in based onthe GPS data from each wearable device 10, as well as the predefinedzones as defined by the location of the boundary markers 240. A sitesupervisor 422 (human or computer-implemented) can monitor which workersare in which zones from one or more control consoles 256 (located in thepersonnel buildings 224 in the embodiment of FIG. 9). The one or morecontrol consoles 256 may be used to view and monitor all of the datareceived by the SST system 200, and in the event one or more zonesrequire evacuation, the site supervisor 422 will be immediatelycognizant (via the SST system 200, the wearable devices 10, and thecontrol console 256) how many workers are within the evacuation areas,as well as their real-time progress in exiting the one or moreevacuation areas. For example, the control console may include a visualdisplay that shows each worker as a dot or an “X” (or other graphicalsymbol) on a site map with the zones overlaid thereon. If it appears asthough one of the X's is not moving even after a site-wide or zone-wideevacuation alert had been issued, it may indicate that the worker inquestion has become injured or impaired, and requires assistance.

The site safety and tracking (SST) system 200 may also be used toincrease worker productivity. For example, each wearable device 10 mayinclude a unique identifier associated with it that corresponds only tothe worker who is wearing the wearable device 10 in question. Each datafile that is transmitted to, and received from, the wearable device 10in question may be tagged with the unique identifier such that theappropriate decisions, data logging, and recording may occur inconnection with the wearable device 10 in question. A “task” or“assignment” may be entered in the SST system 200 for each worker,wearable device 10, and corresponding unique identifier. Severalvariables and pieces of information may be entered into the SST system200 in connection with each task or assignment including the zone inwhich the task is to be carried out, an expected timeframe in which thetask may be accomplished, a description of the task, additional notes orinstructions about the task, as well as other pertinent informationabout the task. The site supervisor 422, using the SST system 200 andthe control console 256, may then track the location of the worker inreal-time and compare the location of the worker to the location of theassigned task. If the location of the worker and the location of theassigned task do not match, the graphical symbol representing the workermay appear in a different color on the control console 256, indicatingthat there may be an opportunity to enhance the productivity of theworker (who may be in the wrong zone) relative to the assigned task.

The SST system 200, in connection with voice command or speechrecognition software and the microphone 32 located on the wearabledevice 10, may enable workers to send verbal descriptions and updates oftheir tasks as they progress. For example, if a worker is required toleave the assigned zone in order to get new supplies, or in order to geta tool that is required for the assigned task and located in a differentzone, the worker may simply use a voice command to send an update thatis logged by the SST system 200. In one embodiment, the worker cansimply say, “Send verbal message: Need tool from warehouse.” Thewearable device 10 may then transmit an audio recording to the SSTsystem 200 or network 56, where the audio recording could be run throughspeech recognition software, converted into text, and logged in thesystem in connection with the assigned task, the date, the time, and theunique identifier associated with wearable device 10 in question.Several benefits of enabling workers to send verbal updates that getlogged in the SST system 200 include: (1) providing a quick and easy wayto allow the worker to provide updates without requiring a laptop ortyping text into one or more electronic devices, (2) providing the SSTsystem 200 (and thus the site supervisor 422) with a reason why theworker is leaving the assigned zone, (3) providing additionalinformation to the SST system 200 detailing why a task is being delayed(which then may be used for future process improvements like ensuringthat the required tools are at the locations in which they are needed),and (4) allowing the SST system 200 to suggest or recommend actions tothe worker based on the update from the worker (for example, if the toolthat is required for the assigned task is located in a closer spot thanwhere the worker is going).

The SST system 200 may also use verbal updates from the workers andwearable devices 10 to track sub-tasks. For example, if a drillingstring has become damaged in a borehole and needs to be replaced orrepaired, a worker can give a verbal update (that is transmitted to theSST system 200) once the drill string has been removed from the borehole(but before any mitigating action has taken place). Updating the SSTsystem 200 via verbal updates on the status of sub-tasks not onlyensures that the SST system 200 is as up-to-date on the status of eachtask as possible (for example, so that predicted completion times can beupdated), but doing so also allows the SST 200 to aid in completion ofthe sub-tasks, where possible. For example, the SST 200 can requestimages or operational videos of equipment (such as a damaged orpartially-damaged drill string) from the wearable device, which can thenbe sent to the network 56 for analysis by the SST system 200, orpossibly by remote parties (for example, other networks within a widearea network (WAN)). In the case of a potentially damaged drill stringthat may be still be functional, remote resources may be used to inspectthe images of the drill string and quickly come to a determination ofthe operability of the drill string, thereby recommending theappropriate action (for example, keep using it, replace it, or repairit) and minimizing unproductive down-time. The SST system 200 can alsouse verbal sub-task updates from workers and wearable devices 10 todeconstruct a big task into several sub-tasks, and to assess how longeach sub-task generally takes. This present opportunities forproductivity enhancements when subtasks are taking longer than expected.Root causes of delays can be determined when sub-tasks are taking longerthan expected. The root cause may then be used to minimize similardelays in the future.

The SST system 200 may interact with and track vehicles, machinery,tooling and other equipment in a similar fashion to how the SST system200 tracks site workers via wearable devices 10, RFID tags (254), orboth wearable devices 10 and RFID tags 254. For example, each tool,machine, vehicle, and other piece of equipment may be assigned awearable device 10 or RFID tag 254 with a unique identifier that allowsit to be assigned to tasks within particular zones, within giventimeframes. The SST system, in connection with the control console 256,may track the locations of each tool, machine, vehicle, and other pieceof equipment and compare them to the locations of the respectiveassigned tasks in order to ensure that everything is where it needs tobe (and to take mitigating actions if they are not). The SST system 200may also categorize and tag each tool, machine, vehicle, and other pieceof equipment according to equipment type and availability status suchthat in the event a particular tool or piece of equipment is needed, theSST system 200 may be searched electronically to determine if a piece ofequipment that matches the type of the needed equipment is available forimmediate deployment at the appropriate zone, et cetera. The SST system200 may also determine that one or more pieces of equipment arerepeatedly not being used, and may redeploy them at other sites wherethey may be of use.

The SST system 200 may be web-based such that control consoles 256 maybe viewable on network or Wi-Fi-connected devices such as tablets,laptops, smartphones, and other electronic devices. The SST system 200may also allow touch-screen-enabled commands for searching forinformation within the SST system 200 (for example, relating to aparticular wearable device 10 or worker), as well as for sendinginstructions, commands, directives, and instructions to various partieswithin the SST system 200. As such, site supervisors 422 may view andcontrol one or more objects or assets within the SST system 200 from aWi-Fi enabled device, without having to be physically located within acontrol room, command center, or other personnel building 224.

The SST system 200 may include speech recognition software that iscloud-based or housed on one or more networks 56 or servers. Forexample, cloud-based speech recognition software may access remote“warehouses” or databases of words, phrases, pronunciations, speechpatterns, and the associated audio signatures of each in order to try tomatch an audio recording with the correct or intended text. Because suchoperations may require large databases and complicated algorithms, itmay be difficult to house an entire speech recognition program on awearable device 10. Instead, a wearable device 10 may include speechrecognition functionality by accessing one or more cloud-based programsvia one or more trigger words. For example, the wearable device 10 mayuse the microphone 32 to “listen” for an audible signature that matchesa predetermined signature that is preloaded onto the wearable device 10.In one embodiment, the wearable device 10 may look for an audiblesignature that matches the phrase “Send verbal message,” and it willthen initiate a microphone recording and send the resulting audio fileto the SST system 200 (or other cloud or network-based system) wherefurther speech recognition steps may be performed. Stated otherwise, byusing one or more trigger words in connection with a microphone andWi-Fi-enabled wearable devices 10, the wearable devices 10 may achievefull speech recognition functionality while housing only a smallfraction of the underlying software, algorithms, and data. When tryingto match an audible or audio signature, the wearable device may look atthe pronunciation of a word or phrase, the audible frequency of eachsyllable, as well as the frequency of each syllable relative to those ofneighboring syllables, in order to compare them to the correspondingattributes of words and phrases within the warehouse or database,assuming common pronunciations. Using a cloud-based speech recognitionsoftware program, the wearable device 10 only needs to make calls to thewarehouse when it “hears” the trigger word or phrase.

In one example, the site supervisor 422 (shown in FIG. 11) may use thecontrol console 256 to identify one or more hazard zones, for exampledue to a gas leak, blow out, explosion, temporary construction, or otherevent. The site supervisor 422, the SST system 200, or both the sitesupervisor 422 and the SST system may identify any worker within theflagged zone and trigger a warning massage (which may include a physicalvibration) to the wearable devices 10 of the involved party or partiesdirectly. Because the SST system 200 is tracking all site personnel andautomatically performing head counts of workers both in the emergencyareas, as well as in the staging, rally, or assembly areas, the SSTsystem may inform the site supervisor 422 exactly how many workers stillneed to be evacuated, as well as the precise location of each worker inthe evacuation area, which makes it easier for a rescue team, should anyworkers require assistance evacuating. Depending on the programmedauthority level associated with a given type of hazard, the SST system200 may send alerts directly to one or more workers via the associatedwearable device 10. In other embodiments or circumstances, the SSTsystem 200 may require confirmation from the site supervisor 422 beforesending out the alert.

In another example, RFID tags 254 or other tracking sensors includingwearable devices 10 may be installed on all regularly moving equipmentsuch as forklifts, cranes, top drive drilling systems, trucks, and othervehicles. Each of the RFID tags 254 and other tracking sensors maytransmit signals to, and receive signals from, each wearable device 10such that if a worker wearing a wearable device 10 gets too close to theequipment, the wearable device 10 will trigger a safety notification orvibration both to the worker wearing the wearing device 10, as well asto the operator of the machinery or vehicle. The SST system 200 may alsoinclude an array of motion sensors 258 (coupled to the rig platform inFIG. 9) disposed at various locations around the worksite to trackmotions and correlate the tracked motions to position data received atthe SST system 200 from the wearable devices 10. The motion sensors 258may be used both as back-ups to the wearable devices (for example tosense if someone has entered an emergency zone), but also to identifypeople and vehicles at the worksite that do not have wearable devices 10or other tracking devices such as RFID tags 254 attached to them. Forexample, if the motion sensors 258 detect motion within a specific zoneor region and the SST system 200 cannot find corresponding equipment,vehicles, or workers in the given region with similar or identicalmovement patterns, the SST system 200 may determine that one or morepeople or vehicles in the region does not have tracking device. In thissituation, the SST system 200 may identify that an animal has enteredthe worksite, especially in embodiments that use one or more cameras asthe motion sensor 258.

In the event of an emergency or disaster, the SST system 200 may becontinually updated on multiple servers in multiple locations includinglocations remote from the worksite, such that all of the latest sitesafety information is always available, even if one or more computers,servers or networks 56 goes down.

In one example, a motion sensor 256 may be disposed on a rig floor orrig platform 202 and may send an alert if a worker is standing too closeto where a drill pipe or tubular is being connected to a drill string.

In another example, the SST system 200 will track the movements ofworkers in and around confined spaces, along with oxygen sensor andother gas readings from oxygen and toxicity detectors installed onwearable devices 10 in order to ensure the confined space continues tobe safe for the workers within the confined space. Alerts may be sent bythe SST system 200 or by the wearable devices 10 to the workers withinthe confined space, as well as to one or more watch people monitoringthe confined space, in the event that the confined space becomes unsafe.

In another example, a motion sensor 256 may be mounted in multiplelocations on an offshore rig where space is limited to ensure thatequipment, vehicles, tools, and cranes or other objects do not impactrig workers, whose locations may be tracked via wearable devices 10.

In another example, a motion sensor 256 or wearable device 10 (or othertracking sensor such as an RFID tag 254) may be mounted to a load thatis being hoisted by a crane such that if a worker gets too close to theload, or if a worker is directly underneath a load or within a projectedpath of a moving load, the SST system 200 will send an alert to allparties involves (for example the crane operator, the worker, and thesite supervisor 422).

In another example, the SST system 200 may send an alert to the relevantparties if a worker enters a restricted zone, or if a worker leaves anassigned zone.

The present disclosed embodiments may include several benefits overexisting solutions. For example, the SST system 200 may promote bothsite safety and site productivity by tracking the movements of theworkers via sensors and wearable devices 10. The SST system 200 may alsocollect otherwise unavailable data from both the several sensors on thewearable devices 10, and also from the other site sensors such as motionsensors 258, RFID tags 254, and boundary markers 240. In addition,worker health information will be tracked which may help in detectingboth short term and long term health concerns.

The SST system 200 may also help to achieve and maintain industry targetsafety standards via a dynamic system that collaboratively integratessensors, wearable devices, and human inputs.

Adaptive Site Safety Systems

The present disclosure describes methods and systems for analyzingcollected data using cognitive reasoning and machine-learning technologythat may operate autonomously. The systems and methods may use differenttypes of data collected by the site safety and tracking (SST) system 200to analyze the data and draw conclusions and recommendations aboutfuture and past worksite events.

FIG. 11 illustrates a machine-learning ecosystem (MLE) 400 includingvarious data inputs 402, a communication module 404, a data warehouse408, a correlation module 410, a decision module 108, a storage module420, a site supervisor 422, various actions 424, and various outputs434. The various data inputs 402 used within the machine learningecosystem 400 may include a wearable device 10A as illustrated in FIG.1, a wearable device 10B as illustrated in FIG. 2, networks 56 (or oneor more networked computers 56), as well as other inputs such as directuser inputs, worksite instrumentation data, data from the data warehouse408, and sensor data (for example, from boundary markers 240, RFID tags254, and motion sensors 258). Each of the various data inputs 402 may becommunicatively coupled to a communications module 404 which may behoused on one or more network computers 56, or any other networkeddevice including laptops and other mobile electronic devices. Thecommunications module may include a multi-band transceiver (for example,a dual-band, tri-band, or quad-band transceiver) for communicating withWi-Fi-enabled devices, as well as GPS satellites, RFID tags, two-wayradios, wearable devices 10, smart phones, networked devices, and otherelectronic devices operating at different frequency bandwidths. Thecommunications module 404 may be communicatively coupled to one or moredata warehouse 408 for storing large quantities of data. In someembodiments, the data warehouse 408 may be communicatively coupled to alocal area network (LAN), which, for example, may include data from allof the networked devices at a worksite. In other embodiments, the datawarehouse 408 may be communicatively coupled to a wide-area network(LAN), which, for example, may include data from all of the worksiteswithin an enterprise. The data warehouse 408 may be located at theworksite or may be remotely located, for example, at a server farm orother storage facility communicatively accessible to every worksitewithin the enterprise. The communications module 404 may perform one ormore pre-processing steps on the data input including (but not limitedto) decompressing the data, parsing the data, filtering the data,collating the data, and filtering the data.

Referring still to FIG. 11, the machine-learning ecosystem (MLE) 400 mayalso include a correlation module 410 for building and refiningcorrelation matrices. Correlation matrices may include neural networks,cognitive reasoning routines, machine-learning algorithms, heuristics,and other forms of artificial intelligence. In one embodiment, thecorrelation module may build one or more correlation matrices thatquantify correlation coefficients or factors that relate one or moreinputs to one or more outputs. The correlation coefficients may be usedwith transfer functions that use one or more parameter inputs to try topredict one or more outputs. The correlation coefficients may be used topredict the value of an output as well as the likelihood of a particularoutcome occurring. For example, the correlation module 410 (inconnection with the correlation coefficients) may be used to predict howlong a project, task, or sub-task will take to complete, given one ormore input parameters. The correlation module 410 may also be used topredict the likelihood that an accident will occur in the course ofcompleting a project, given one or more input parameters. Thecorrelation module 410 may divide data sets up into subsets and look atranges of data that include several inputs and several outputs (forexample, a single data set including input data sets from multiplewearable devices, facility instrumentation, and site location trackingsensor information, as well as output data including project completiontime, and information regarding whether or not an accident occurred).The correlation module 410 may look at several such data sets to assessand quantify which input parameters are strong predictors of outputvalues or output conditions.

Still referring to FIG. 11, the correlation module 410 may use any curvefitting, polynomial equation, transfer function, Gaussian distributionanalysis, non-Gaussian distribution analysis, probability theory,regression analysis, interpolation, extrapolation, Bayes estimators, orother numerical, logical, scientific or other method or algorithm forrelating inputs to outputs. The correlation module 410 may employvarious curve-fitting techniques for relating a single input parameterto a single output parameter. The correlation module 410 may alsocombine various combinations of input parameters into a transferfunction that may be predictive of one or more output parameters. Thecorrelation module 410 may use one or more first output parameters topredict one or more second output parameters (for example, is the timeto complete a project (often an output parameter) predictive of thelikelihood that an accident will occur (also often an output parameter)or vice-versa?) The correlation module 410 may also generate randomsamples of data (for example, sub-sets of a larger dataset) to test therobustness of a given curve fit or prediction model. For example, if thecurve fit is only a good predictor of an outcome based on the set ofdata upon which it was developed, it may not be a very robust curve fit.By contrast, if the curve fit is a good predictor of an outcome based onseveral different non-overlapping data sets, each with statisticallysignificant numbers of data points, it may be a robust curve fit. Thecorrelation module 410 may therefore employ dozens of differentcurve-fitting routines, using hundreds of parameters, thousands ofpossible parameter combinations, as well as thousands or millions ofpossible randomly-generated datasets upon which to develop and test therobustness of algorithms, the datasets themselves continuously beingupdated to include new data.

Referring still to FIG. 11, the correlation module 410 may include onecomputation-specific hardware components. For example, because thecorrelation module 410 may repeatedly perform curve-fitting algorithms,possibly in parallel on multiple data sets simultaneously, computerhardware designed for parallel processing of specific routines mayenhance the performance of the machine-learning ecosystem (MLE) 400. Forexample, the correlation module 410 may include one or more graphicsprocessing units (GPU) 412 programmed to perform curve fitting and othercorrelation or matrix-building algorithms. In another embodiment, thecorrelation module 410 may include one or more field-programmable gatearrays (FPGA) 414. In another embodiment, the correlation module 410 mayinclude one or more application-specific integrated circuits (ASIC) 416.The correlation module 410 may include one or more GPU 412, one or moreFPGA 414, one or more ASIC 416, or any combination of GPU 412, FPGA 414,and ASIC 416 arrangements. In addition, different functions (forexample, curve-fitting routines, recording correlation factors,generating data sub-sets, testing curve fits, and other functions) maybe assigned to different components (for example, the one or more GPUs412, the one or more FPGAs 414, and the one or more ASICs 416). In oneembodiment, the correlation module 410 may include several ASICs 416arranged in a climate-controlled environment where the temperature iscontrolled so as to not exceed 95 degrees F. (35 degrees C.), each ASIC416 including a dedicated cooling fan (or other cooling mechanism) andaccommodating from about 500 W to about 3000 W input power, and fromabout 110V to about 240V input voltage. In another embodiment, one ormore ASICs 416 may accommodate input powers from about 1200 W to about1600 W, and input voltages from about 220V to 240V. In anotherembodiment, one or more ASICs 416 may accommodate an input power ofabout 1480 W, +/−20 W.

Still referring to FIG. 11, the correlation module 410 may be coupleddirectly to the data warehouse 408 such that the correlation module 410may make frequent calls to the data warehouse to retrieve data sets touse in building correlation matrices. The correlation module 410 mayalso be communicatively coupled directly to the communications module404 such that new data received by the communications module 404 mayimmediately be used by the correlation module 410. Similarly, thecorrelation module 410 may include memory or storage housed within thecorrelation module 410, and may also access system storage 420 that mayalso be accessible to other modules such as the decision module 108.Therefore, the correlation module 410 may include (or have access to)three (3) or more types of memory: cache or buffer memory for short termstorage (for example, for storing intermediate parameters, variables,coefficients, and other data while a computation is in process); systemmemory (or intermediate-term) for storing correlations, data, orprediction models that have been confirmed, have yet to be confirmed, orhave been rejected (but are being stored for tracking purposes andfuture possible refinement); and, long term storage at the datawarehouse for logging and indexing data that does not often need to beused or called, but that nonetheless is beneficial to retain.

Referring still to FIG. 11, the correlation module 410 may becommunicatively coupled to the decision module 108, which may includeone or more central processing units (CPU) 418. The decision module 108may be used to decide which algorithms, prediction models, correlations,and recommendations from the correlation module 410 to keep and which todiscard or save for later evaluation. The decision module 108 may useany of several different factors to determine which algorithms,prediction models, correlations, and recommendations from thecorrelation module 410 to keep and which to discard. For example, inevaluating the robustness of the algorithms, prediction models,correlations, and recommendations from the correlation module 410, thedecision module 108 may look at r-squared values, confidence intervals,the likelihood of occurrence of an event (that is, as predicted by oneor more prediction models), the consistency of a prediction model usingdifferent test data sets, how many data samples the prediction model isbased on, whether or not the prediction model has been tested usingdifferent data sets, the quality of the data set or sets upon which theprediction model was based, assessing if the prediction model uses anover-constrained (or over-fitted) model that is too tailored to match aspecific data set (for example, using a higher order polynomial fit suchas fourth-order or higher), and whether the prediction model has provenaccurate after a cursory or initial implementation, as well as otherpossible factors and determinants. Each of the one or more factors thatthe decision module 108 may use to confirm the robustness of analgorithm, correlation, recommendation, or prediction model from thecorrelation module may be computation-intensive. As such, the decisionmodule may also include at least one of a GPU 412, an FPGA 414, and anASIC 416. Similarly, the correlation module 410 may include at least oneCPU 418 for dictating the order or operations that the correlationmodule 410 should follow when building correlation matrices, and alsofor governing the overall functionality of the correlation module 410.The decision module 108, similar to the correlation module 410, mayinclude internal memory, and may also be coupled to the storage module420, system memory or shared memory, as well as to the data warehouse408.

Still referring to FIG. 11, the decision module 418 may use heuristicsto confirm the robustness of a prediction model from the correlationmodule 410. For example, the decision module 108 may generally belooking for: curve fits with an r-squared value of at least 0.9,prediction models with a confidence interval of at least 80%, data setswith at least 60 data points, and algorithms or prediction models testedon at least 2 non-overlapping data sets. Using heuristics, the decisionmodule 108 may decide that a prediction model that includes a curve fitwith an r-squared value of 0.85, a confidence interval of 70%, and analgorithm that was tested on only a single data set, but is based onover 6,000 data points, is sufficiently robust to justify confirmationby the decision module 108. Stated otherwise, even though the predictionmodel did not include three (3) of the four (4) desired factors, it wasclose to the desired levels on the three (3) it did not include, and itgreatly exceeded the threshold for the fourth factor (that is, 6,000data points). Similarly, the decision module 108 may decide that a dataset that includes all of the desired factors (that is, in this example,an r-squared value above 0.9, a confidence interval above 80%, at least60 data points, and an algorithm that has been tested on two or morenon-overlapping data sets), is nevertheless not robust if, for example,it includes an over-fitted curve fit (for example a tenth (10^(th))order polynomial fit) that the decision module 108 determines not to betruly predictive of the relationship between input and outputparameters. Stated otherwise, by examining the proposed prediction modelas a whole, the decision module 108 may determine that the model was notsufficiently robust, even though it included all of the enumerateddesired characteristics, in this example. As such, using heuristics, thedecision module 108 may stray from a predetermined set of requirementsin deciding whether to confirm or reject a prediction model, and maymake decisions based on the data set as a whole, which may not requirethat a hard set of rules be adhered to in every case. Both the decisionmodule 108 and the correlation module 410 may use one or more thresholdchecks (r-squared values, confidence intervals, et cetera) in decidingwhich correlations and prediction models to promote, and which to deferor reject. Checks executed at the decision module 108 may also beconsidered to be “verification checks,” as they serve to verify that theprediction models promoted by the correlation module 410 to the decisionmodule 108 are sufficiently robust.

Referring still to FIG. 11, once the decision module 108 confirms therobustness of a prediction model, correlation, algorithm, orrecommendation, it may promote it to the site supervisor 422, where theprediction model, correlation, algorithm, or recommendation may beaffirmed. The site supervisor 422 may include one or more controlconsoles 442 that may display information to one or more users and mayalso act as a human interface for the MLE 400. For example, one or morerecommended actions from the decision module 108 may be displayed on theone or more control consoles 442 allowing a user to affirm that arecommended decision should be taken, or alternatively to reject therecommended action. The one or more recommended actions may include adescription of the recommended action, as well as a summary of theunderlying data upon which the recommended action is based. In oneembodiment, a user may be able to click on (or tap, in embodiments thatemploy a touch screen) the recommended action and see details of theunderlying data upon which the recommended action is based, which may befactored in to the decision by the user on whether or not to affirm therecommended action. The underlying data included in the summary mayinclude r-squared values, confidence intervals, the number of datapoints the recommendation is based on, how many data sets therecommendation has been tested against, details on the curve fit, aswell as other pertinent information like historical data relating topast results arising from actions similar to the recommended action.

Still referring to FIG. 11, once a recommended action has been affirmedby the site supervisor 422, one or more actions 424 may be implementedincluding, but not limited to: sending alerts 426 to one or more devicesincluding wearable devices 10A, 10B, as well as to site publicannouncement systems and display screens; dispatching one or more rescuecrews 428 to an emergency area or to tend to an injured or disabledworker; reassigning one or more crew members 430 to a differentassignment or possibly to a different crew in order to improve theeffectiveness or efficiency of a crew or project, or to reduce thelikelihood of an accident occurring; and repositioning 432 equipment toenhance worksite productivity or to reduce the likelihood of anaccident. Other actions 424 may include removing equipment from service(due to needed repairs or the equipment reaching the end of its usefullife), evacuating an off-shore oil-rig due to predicted adverse weatheror predicted impending failure of one or more components or structures,temporarily or permanently suspending one or more employees from theirassigned task (or reassigning them to other tasks) due to safetyconcerns associated with their current assignments, and immediatelyceasing work on a project or a portion of a project due to a safetyconcern or risk associated with the project (and then subsequentlytaking mitigating efforts to address the safety concern in question). Asa result of the one or more implemented actions 424, one or more outputs434 for example improved safety 436 or improved productivity 438 mayoccur (either immediately, or over longer periods of time). Improvingsafety at a worksite may include improving at least one safety metric,which may include at least one of: reducing the likelihood of anaccident; increasing the number of days since the occurrence of theprevious accident at the worksite; increasing the number of lost hoursor days due to an accident on a weekly, monthly, quarterly, or yearlybasis; decreasing the number of accidents per week, month, quarter, oryear; as well as other suitable safety metrics. Improving productivityat a worksite may include one or more of: reducing the amount of time atask or sub-task takes to complete; reducing the overall installation,construction, or build time of a project; reducing the amount of workersrequired to complete a project, task, or sub-task; reducing the amountof rework required to complete a project, task, or sub-task; increasingthe amount of projects, tasks, or sub-tasks accomplished within a giventimeframe; and other suitable productivity metrics. The one or moreoutputs 434 represent results that the machine-learning ecosystem (MLE)400 is ultimately directed at, or aiming to achieve or improve.

Referring still to FIG. 11, the one or more control consoles 442 mayinclude a display viewable on one or more computer monitors, as well asa webpage viewable on desktop computers, laptops, tablets, smartphones,and other electronic devices, or a display viewable via applicationsoftware. The one or more control consoles 442 may be viewable onmultiple devices simultaneously and may require users to enter a loginand password in order to access it. The one or more control consoles 442may have multiple access levels so that at one level, one or more firstusers may have read-only or view-only access allowing them to view andpossibly download information from the one or more control consoles 442for status reporting or further analysis. At a second access level, oneor more second users may have permission to write or enter informationand data into the MLE 400 via the one or more control consoles 442. At athird access level, one or more third users may have the authority toinitiate actions by affirming (or alternatively rejecting) one or morerecommendations from the decision module 108. The one or more controlconsoles 442 may be accessed and interacted with via a mouse andkeyboard, or alternatively via a touchscreen, or via both a mouse andkeyboard and a touchscreen. In other embodiments, the one or morecontrol consoles 442 may also be accessed and interacted with via speechrecognition and voice commands (the MLE 400, in some embodiments,verifying the user access level via recognition of the voice or voicesof the individual user(s)). At 440, the site supervisor 442 may transmitan update (for example, relating to an update on a project or the statusof a piece of equipment) directly to the communications module 404. Assuch, one or more inputs 402 to the MLE 400 may be received by thecommunications module directly from the site supervisor 422.

Still referring to FIG. 11, the recommended actions or prediction modelsthat are transmitted from the decision module 108 to the site supervisor422 may include: remaining useful life estimates (for example in hours,days, weeks, months, or years) for various pieces of equipment;estimates for the likelihood of an accident occurring within a giventimeframe; suggestions for reducing the likelihood of an accidentoccurring; proposed root causes of a delay or accident including acalculation of the chances, odds, or probabilities that each proposedroot cause is the actual root cause (or a contributing root cause); oneor more suggestions for a productivity improvement; predicted completiontime(s) for one or more projects; real-time or near real-time projectstatuses, the locations of various equipment and personnel within a jobsite; as well as other potentially useful recommendations. In someembodiments, the prediction module 108 may produce only a finite numberof recommendations at a given time (for example, only ten (10)recommendations at a given time or only 5 new recommendations per day).The recommendations may be ranked according to one or more rankingsystems, thereby allowing the decision module 108 to transmit thehighest ranked recommendations to the site supervisor 422.

Referring still to FIG. 11, each time a recommendation or predictionmodule is rejected at the decision module 108 as well as at the sitesupervisor 422, feedback may be sent so that the algorithms can begin tolearn which factors are most important, and those factors can beweighted more heavily in the building of future correlation matrices,prediction models, and recommendations. For example, the site supervisor422 may provide feedback to the decision module 108 (and the decisionmodule 108 may in turn provide feedback to the correlation module 410)such as “not enough data” (meaning the underlying data on which theprediction model or recommendation was based is insufficient), or“inconsistent data,” (meaning that one or more attributes of theunderlying data set (for example, the standard deviation or dataresolution) results in the quality of the data being brought intoquestion). The site supervisor 422 may base the feedback on the datasummary that the decision module 108 transmits accompanying arecommendation, thereby illustrating the basis for the recommendation(or prediction model). As the decision module 108 and correlation module410 accumulate more feedback, both on the types of recommendations thatare confirmed or affirmed, as well as on the types of recommendationsthat are rejected, metadata (including the number of data points,r-squared values, polynomial fit details, confidence interval, etcetera) can be collected to identify characteristics that are morelikely to result in a confirmed or affirmed recommendation or predictionmodel.

Still referring to FIG. 11, after sufficient feedback has been received,each of the decision module 108 and correlation module 410 may thenbegin to quantify a likelihood or probability that a given predictionmodel or recommendation will be affirmed or confirmed, and rank themaccordingly. For example, in quantifying the likelihood or probabilitythat a prediction model or recommendation will be confirmed, each of thedecision module 108 and correlation module 410 may assess the individuallikelihoods one characteristic at a time (for example, recommendationsthat include an r-squared value of 0.7 are confirmed 45% of the timewhile recommendations based on 2500 data points are confirmed 62% of thetime). The probabilities for each of the individual characteristics maythen be combined to create an aggregate score describing the overallprobability of the prediction model or recommendation being confirmedbased on all of the characteristics in aggregate. Each of the decisionmodule 108 and correlation module 410 may then rank each of theprediction models and recommendations based on the aggregate score, andonly promote those prediction models and recommendations that have aprobability of being confirmed above a predetermined threshold (forexample, only promoting recommendations or prediction models with a 40%or higher probability of being confirmed by the site supervisor 422).Other ranking systems may include the ranking of prediction models andrecommendations based on the criticality of an expected result (forexample, a prediction of an impending catastrophic equipment failure),or based on the magnitude of an expected benefit (or loss prevention).

Still referring to FIG. 11, as both the decision module 108 and thecorrelation module 410 receive more feedback from the site supervisor422, the prediction models may continue to be refined based on inputsfrom one or more human site supervisors interacting with themachine-learning ecosystem 400 via the control console 442 and the sitesupervisor 422. Over time, both the decision module 108 and thecorrelation module 410 will increasingly incorporate feedback resultingfrom the human interaction at the site supervisor 422, to help the MLE400 “learn,” or cognitively adapt the respective algorithms andcorrelation-building routines. As the algorithms and routines becomemore refined, the site supervisor 422 may be given authority toautonomously confirm or reject various recommendations and predictionson its own, without any human intervention. For example, certain typesof recommendations or prediction models may be confirmed or rejectedautonomously by the site supervisor 422. In other embodiments,recommendations and prediction models that have a high calculatedlikelihood of being confirmed (for example, those with probabilitiesgreater than 95%) may similarly be autonomously confirmed by the sitesupervisor 422 without requiring human intervention. As such, decisionmaking at the site supervisor 422 may gradually be transitioned fromprimarily human-based to more autonomous or machine-based, as theconfidence in the recommendations and prediction models increases overtime. Thus, for certain functions, the site supervisor 422 may beintegrated into the decision module 108. In refining prediction models,recommendations, correlation matrices, and algorithms, each of thecorrelation module 410 and the decision module 108 may re-run or rebuildcorrelations based on new data sets, or with certain data sets andsub-sets removed, based on feedback from the site supervisor 422. Theprediction model, recommendations, correlation matrices and algorithmsmay also be refined such that they weight more recent data more heavilyor such that they emphasize enterprise data from similar sites overenterprise data from sites that are less similar to the site inquestion. The correlation module 410 may also import existing predictionmodels, correlation matrices, recommendations, and algorithms from othersites (that is, by downloading them from the data warehouse 408) to useas a starting point upon which refinements can be made from updated sitedata, rather than trying to build the prediction models, algorithms, andcorrelation matrices from scratch.

Referring still to FIG. 11, each of the communications module 404, thedata warehouse 408, the correlation module 410, the decision module 108,and the site supervisor 422 may be housed on one or more dedicatedcomputers, all on a single shared computer, all on one or more networks56, as well as various combinations thereof. Each of the communicationsmodule 404, the data warehouse 408, the correlation module 410, thedecision module 108, and the site supervisor 422 may also include adatabase management system (for example SQL) for creating appropriatedata structures, querying various databases, managing datasets, taggingunderlying data with the appropriate metadata, and various otherfunctions. The machine-learning ecosystem (MLE) 400 may producerecommendations and predictions that do not need to be 100% accurate tobe useful. For example, if a recommendation has only a 30%, 20%, or evena 10% probability of being accurate (or is only accurate 10% of thetime), over time, those recommendations, if acted upon, maysignificantly reduce accidents, and may lead to significant improvementsin worksite productivity.

FIG. 12 illustrates an example of a correlation matrix or predictionmodel build process or method 406 as carried out by the correlationmodule 410, as well as interaction between the correlation module 410and the decision module 108. At step 444, the process 406 may includereceiving, at the correlation module 410, input data from one or moredata sources 402 including data from wearable devices 10, networks 56,sensors 254, 240, 258, data warehouses 408, site supervisors 422, andother sources. At step 446, the process 406 may include identifying,within the data, input parameters (such as worker location, heartratedata, assigned task information, humidity data, gas detector data,accelerometer data, as well as other parameters), as well as outputparameters (for example task completion time, and the occurrence (orlack of occurrence) of an accident, among other possible outputs). Atstep 448, the process 406 may include identifying the first output 448around which correlations and prediction models may be built by thecorrelation module 410. For example, the first output 448 may includethe occurrence of an accident, such that the correlation module 410 mayattempt to identify which input parameters are correlated with theoccurrence of an accident. At step 450, the process 406 may includedividing the data set up into one or more subsets that may be used foridentifying correlations between input parameters and output parameters,as well as for verifying the correlations. For example, a first subsetmay be used to identify a correlation, while a second subset could beused to test the correlation (that is, to see if the correlation existswithin the second subset as well).

Referring still to FIG. 12, at step 452, the process 406 may includeassessing the one or more data subsets for first order effects. Firstorder effects may be any correlations that can be established between asingle input parameter and a single output parameter. In this example,an output parameter that includes a quadratic dependence from a singleinput parameter would be considered to be a first order effect. Thecorrelation module 410 may systematically check each input parameteragainst the first output parameter to assess what first order effects orcorrelations may exist. In assessing whether one or more first ordereffects may exist, the correlation module 410 may use any curve fittingmethodology, polynomial equation, transfer function, Gaussiandistribution analysis, non-Gaussian distribution analysis, probabilitytheory, regression analysis, interpolation, extrapolation, Bayesestimators, or other numerical, logical, scientific, or other method oralgorithm for relating inputs to outputs. At step 454, for each firstorder effect, the process 406 may include quantifying a confidence levelor r-squared value to rank how accurately the output parameter can bepredicted from each input parameter individually. At step 456, theprocess 406 may include assessing which correlations or first ordereffects meet one or more predetermined thresholds (for example, anr-squared value of more than 0.9, or a confidence interval of more than80%). If the correlation or first order effect meets one or morethresholds, the process 406 may include, at step 464, assessing if thecorrelation is a duplicate of a previously established correlation. Forexample, ambient temperature (input parameter) may be positivelycorrelated with the occurrence of site worker heat stroke (outputparameter). But dew point temperature may also be positively correlatedwith worker heat stroke at approximately the same magnitude. Bothcorrelations do not need be added, necessarily, to a prediction modelsince either parameter may be equally predictive as using both. In someembodiments, even though both ambient temperature and dew pointtemperature are positively correlated with the occurrence of heatstroke, dew point temperature may be a more accurate predictor of heatstroke, thereby obviating the need to also include a correlation basedon ambient temperature in a prediction model. If the first order effector correlation is not a duplicate, a new correlation has likely beenidentified, and the correlation module 410 may update the correlationmatrix or prediction model at step 466 of the process 406.

Still referring to FIG. 12, if there is no correlation for a givenparameter that meets the predetermined threshold at step 456, thecorrelation module 410 may assess at step 458 if the parameter presentlybeing evaluated is the n^(th) parameter (that is, the last inputparameter in the data set or subset). If it is not, the correlationmodule 410 may assess first order effects again at step 452 based on a2^(nd), 3^(rd), 4^(th), 5^(th), and higher ordinal numbered inputparameter. At step 458, if the parameter presently being evaluated isthe n^(th) parameter, the process 406 may include proceeding to step 462where second order, third order, fourth order, and higher ordinalnumbered order effects may be assessed (similar to the first ordereffects assess at step 452). A second order effect, in this instance, isone that includes at least one output parameter being correlated to twoinput parameters. One or more transfer functions may be used to relatethe two input parameters to the output parameter. Similarly, transferfunctions may be used to relate three input parameters to the outputparameter, when assessing third order effects. The correlation module410 may use Laplace transformations, differential equations,computer-generated transfer functions, Fourier approximation, and othernumerical methods to create second order, third order, fourth order, andhigher ordinal numbered transfer functions. The process 406 may includerepeating steps 454, 456, 464, and 458 on the second order, third order,fourth order, et cetera transfer functions.

Referring still to FIG. 12, first order and higher order effects thathave been assessed for each of the input parameters and combinations ofinput parameters, at step 466 of the process 406, may include updatingthe correlation matrix or prediction model based on the correlationsthat meet the one or more thresholds at step 456 (assuming they're notduplicates of existing correlations). At step 468, the process mayinclude verifying each subset against a second data subset, to ensurethat it is robust and predictive based on data sets other than just thedata set from which it was derived. Verifying each subset against asecond or third data subset may include calculating r-squared values, orconfidence intervals based on the second or third data subset andcomparing them to those of the first data subset to determine if theyare consistent across data subsets, or if the r-squared values andconfidence intervals drop in the second and third data subsets comparedto the first subset. In some embodiments, verifying each correlationwith other subsets (at step 468) may occur prior to updating thecorrelation matrix or prediction model (at step 466). After all firstorder, second order, third order, fourth order, fifth order, et ceteraeffects have been identified, quantified, and tested for the firstoutput parameter, the process 406 may include returning to step 448 toidentify a second output parameter in the data set. Steps 450-468 maythen be repeated for a second output parameter, and then subsequentlyfor third, fourth, and fifth outputs, et cetera. Each of the inputs andoutputs may be tagged as inputs and outputs, respectively. In otherembodiments, the correlation module may be programmed to identify whichparameters are outputs, and which parameters are inputs. In someembodiments, metadata may be used as (or may include) input parameters,while in other embodiments, metadata may include outputs. In otherembodiments, metadata may include both inputs and outputs. At step 470,the process may include making recommendations based on correlationsthat prove to be sufficiently robust at step 468, and transmitting themto the decision module 108 for confirmation.

Still referring to FIG. 12, the decision module 108, at step 472, mayevaluate recommendations received from the correlation module 410. Inevaluating each recommendation or prediction model from the correlationmodule 410, the decision module 108 may calculate an aggregate scorebased on one or more of: r-squared values, confidence intervals, thenumber of data points within the data set(s) used, the number of datasets used, the underlying data quality, details of the curve-fittingequation or transfer function, as well as other attributes of theprediction model or recommendation, and underlying data sets thereof. Atsteps 474, 476, and 478, the method 406 may include confirming,rejecting, or deferring a decision on the recommendation or predictionmodel received from the correlation module 410. The decision by thedecision module 108 to defer a recommendation or prediction model may bedue to the recommendation falling in a “gray area” where it is notstrong enough to be clearly confirmed, nor weak enough to be decisivelyrejected. A deferred recommendation or prediction model may be furtherrefined at the correlation module 410 to attempt to ultimately allow itto be confirmed at the decision module 108. In other embodiments, thedeferred recommendation or prediction model may simply be held for aperiod of time and eventually confirmed without further refinement, ifno stronger recommendations are generated by the correlation module 410.In other embodiments, a deferred recommendation or prediction model maybe automatically confirmed or rejected if no action has been taken on itafter a predefined period of time. At step 480, and following aconfirmation, rejection, or deferral of the recommendation or predictionmodel by the decision module 108, the process 406 may include sendingfeedback (at step 480) from the decision module 108 back to thecorrelation module 410. In one embodiment, the feedback may include onlyinformation about the decision (that is, confirm, reject, or defer) andthe recommendation or prediction model to which it pertains. Thisinformation alone may enable the correlation module to refine futurerecommendations and prediction models to increase the likelihood ofgetting a confirmation at the decision module 108. In other embodiments,the feedback sent to the correlation module 410 may also includeinformation or data outlining the specific reason or reasonscontributing to the decision that was made at the decision module 108.At step 482, the process 406 may include initiating at least one action(which may be executed in connection with the site supervisor 422). Theat least one action may include sending out an alert, dispatching one ormore rescue crews, reassigning crew to another task, repositioningequipment within the worksite, as well as other actions, as disclosed inthe present embodiments.

Referring still to FIG. 12, the process 406 may include additional stepsthat are not illustrated. In addition, the steps may be performed in adifferent order than what is shown in FIG. 12. One or more steps mayalso be repeated or omitted, according to aspects of the presentembodiments.

In one embodiment, the machine-learning ecosystem (MLE) 400 may identifymetadata to temporarily withhold from the correlation module 410 toallow the correlation module to predict one or more aspects of themetadata in order to test the accuracy of prediction models orrecommendations, which may then be verified and refined as needed whenthe metadata becomes available to the correlation module 410. Forexample, if a site worker transmits one or more images to the sitesafety and tracking (SST) system 200 accompanied by verbal descriptionsrecorded via the wearable device 10, the correlation module 410 may thenview the image and attempt to identify one or more objects within theimage, as well as the condition of the one or more objects orcomponents. For example, the image may include a picture of a damagedpump and the correlation module 410 will be tasked with identifying 1)that there is a pump in the image, and 2) that the pump is damaged. Whenthe correlation module 410 has finished inspecting the image andattempting to provide a prediction or assessment of the image, the SSTsystem 200 may then transmit thereby allowing the correlation module 410to verify the accuracy of the assessment (and to refine the algorithm asneeded). The communications module 404, the data warehouse 408, and thedecision module 108 may all be used to systematically remove metadatafrom data inputs sent to the correlation module 410, to increase theopportunities for the prediction models and algorithms to “learn” orbecome more refined or accurate. In addition to descriptions of images,other types of metadata that may be used to enhance and refineprediction models may include: location data (which can be verifiedagainst visual images of known locations at the worksite), assigned taskstatus updates, information about whether or not an accident occurred,information on how long a task or sub-task took to complete, as well asother data and information.

In another embodiment, each or any of the communications module 404, thedata warehouse 408, and the decision module 108 may provide choices tothe correlation module 410 to choose from, for example “pump,” “ironroughneck,” “generator,” or “tubular.” The correlation module 410 maythen inspect the image and provide a probability for each option, forexample “pump: 84%, generator: 9%, iron roughneck: 5%, tubular: 2%,”indicating that (in this instance) the correlation module 410 predictsthat the image has an 84% probability of depicting a pump, a 9% chanceof depicting a generator, et cetera. The probabilities may then beverified, and if necessary, refined once the metadata describing theobject or objects in the image is revealed to the correlation module410.

In another embodiment, each or any of the communications module 404, thedata warehouse 408, and the decision module 108 may strip the uniqueidentifier data (that is, the identity of the site worker or wearabledevice 10) out of the data set and the correlation matrix 110 may betasked with identifying the site worker based on a heartrate signatureor voice recognition software. In another embodiment, the correlationmatrix 110 may be tasked with identifying when a site worker is runningwithout using GPS or location-tracking data (which may be stripped outof the data set) by looking at the accelerometer data to sense morefrequent footsteps, or by looking at the heartrate data. In anotherembodiment, the correlation matrix 110 may be tasked with inspectingdata of a site worker walking around a site for an extended period oftime with no noticeable or traceable heartbeat. The correlation matrix110 may either recognize autonomously or may be programmed to recognizethat in this situation, it is more likely that the heartrate monitor 42has become disconnect or removed from the site worker, than it is thatthe site worker's heart has actually stopped beating (that is, while thesite worker continues to walk around). As such, the SST system 200 maysend a message or alert to the worker via the wearable device 10 toreinstall the heartrate monitor.

In another embodiment, the SST system 200 may trend a worker's heartrateover time and identify and track characteristics such as averageheartrate, maximum heartrate, as well as resting heartrate, on a dailybasis. Resting heartrate has been shown to be correlated with howfatigued a person is. The lower the resting heartrate (that is, relativeto the individual's normal resting heartrate), the more rested theindividual. The higher the resting heartrate (again, relative to theindividual's normal resting heartrate), the more fatigued the individuallikely is. The SST system 200, in connection with the MLE 400, mayrecognize that an individual's resting heartrate is much higher thannormal and may identify that worker fatigue is correlated withaccidents. The SST system 200 may send an alert or warning, which mayresult in the worker being redeployed to a different assignment, orinstructed to temporarily suspend work, so as to allow the worker torest.

In another embodiment, the SST system 200, in connection with the MLE400, may receive update or status data from one or more workers as aproject or assignment is progressing. The status data may include verbalmessages sent by one or more workers via one or more wearable devices 10to the SST system 200 detailing various milestones and sub-taskinformation. Over time, the SST system 200, in connection with the MLE400, may accumulate enough information to quantify how long each type ofassignment, project, job, task, or sub-task is expected to take. Thisinformation may also be available via a WAN, data warehouse 408, orother source, based on similar worksites in the enterprise. The MLE 400(in connection with the correlation module 410 and decision module 108)may track how long each worker or crew takes to do each task orsub-task, and may compare that information to benchmark values. The MLE400 (in connection with the correlation module 410 and decision module108) may recognize that certain crews are more productive at certaintasks than others and may use this information to identify areas ofimprovement for various individuals and crews, and may also use thisinformation for assigning crews to the work tasks for which they aremost productive or efficient.

In another embodiment, the SST system 200, in connection with the MLE400, may recognize (for example, at a construction worksite) thatschedule delays, accidents, or both schedule delays and accidents may becorrelated with where equipment (such as cranes, forklifts, trucks,vehicles, and other equipment) is located on the site. In some cases,the correlations may be real, meaning that the placement of certainequipment at the worksite is somehow causally linked to one or moreoutputs (for example, delays or accidents). In other cases, thecorrelations may actually be coincidences that are not causally linkedin any way. The SST system 200, in connection with the MLE 400, may useconfidence intervals (such as a an 80%, 90%, or 95% confidence interval)as well as p-values (such as p-values equal to or less than 0.05, 0.10,or 0.15) to quantify which links are statistically significant, therebydistinguishing between true correlations, and mere coincidences.

The SST system 200, in connection with the MLE 400, may identifypatterns of behavior in historical accidents or in more recent data, bycorrelating incidents to a variety of data types including managementactivities, facility inspection findings, leadership engagements, recentreported observations, daily site operations data, and trainingnon-conformities. When reoccurring patterns of behavior are observedthat correlate to various types of accidents, incidents, andproductivity losses, early warnings, alerts, and red flags may begenerated by the SST system 200. These early warnings and alerts mayprovide users with insights relating to specific contractors, equipment,project types, facilities, worksites, and other considerations that areoperating under known conditions that have historically caused injuries,incidents, equipment damage, or property damage. Recommendations andcorrective actions based on historical incident investigations and morerecent operating data may be generated by the SST system 200 to preventthe predicted incidents from occurring. By allowing the SST system 200,in connection with the MLE 400, to autonomously analyze and recognizepotential hazards, the SST system 200 effectively creates a forcemultiplier within a worksite or even an entire enterprise or company, byaugmenting traditional site safety systems and personnel, and allowingworksite personnel to focus on prevention. As such, the SST system 200allows the enterprise to become more proactive in nature, therebyreducing the largely reactive manner in which many health and safetyorganizations operate.

The site safety and tracking (SST) system 200 may be used at worksitesin connection with wearable devices 10 and a machine-learning ecosystem(MLE) 400. As worksite operational data is collected, trended, andexamined by the correlation module 410 and the decision module 108,prediction models may be developed and refined such that the sitesupervisor 422 may gradually transition from a human-authority system,to one that offloads many decisions to computer-based systems, therebygiving the MLE 400 the authority to autonomously make more and moretypes of decisions. The machine-learning ecosystem (MLE) 400 may be“trained” by trending the types of prediction models and recommendationsthat are confirmed or rejected by the site supervisor 422, and also viahuman-interface inputs through verbal metadata received via one or morewearable devices 10, and also through feedback from the site supervisor422, the decision module 108, and other sources. The machine-learningecosystem (MLE) 400 may also train itself by building correlationmatrices relating one or more input and output parameters, usingenterprise data from other sites, as well as data collected at the localsite. As such, an intelligent worksite safety and tracking system 200with machine-learning functionality may be autonomously built whileusing the SST system 200, by integrating human-computer interfaces andmachine-learning into everyday operations and activities, allowing forcontinuous refinements to prediction models, correlation matrices, andthe like, according to the present disclosed embodiments.

Each of the instruments, devices, and sensors described in the presentdisclosure may include a wired power supply or a wireless power supplysuch as a battery, capacitor, or other suitable mechanism.

All or part of the system and processes described in this specificationand their various modifications (subsequently referred to as “theprocesses”) may be controlled at least in part by one or more computingsystems using one or more computer programs. Examples of computingsystems include, either alone or in combination, one or more desktopcomputers, laptop computers, servers, server farms, and mobile computingdevices such as smartphones, feature phones, and tablet computers.

The computer programs may be tangibly embodied in one or moreinformation carriers, such as in one or more non-transitorymachine-readable storage media. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed as a stand-alone program or as amodule, part, subroutine, or unit suitable for use in a computingenvironment. A computer program can be deployed to be executed on onecomputer system or on multiple computer systems at one site ordistributed across multiple sites and interconnected by a network.

Actions associated with implementing the systems may be performed by oneor more programmable processors executing one or more computer programs.All or part of the systems may be implemented as special purpose logiccircuitry, for example, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC), or both.

Processors suitable for the execution of a computer program include, forexample, both general and special purpose microprocessors, and includeany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only storagearea or a random access storage area, or both. Components of a computer(including a server) include one or more processors for executinginstructions and one or more storage area devices for storinginstructions and data. Generally, a computer will also include one ormore machine-readable storage media, or will be operatively coupled toreceive data from, or transfer data to, or both, one or moremachine-readable storage media.

Non-transitory machine-readable storage media include mass storagedevices for storing data, for example, magnetic, magneto-optical disks,or optical disks. Non-transitory machine-readable storage media suitablefor embodying computer program instructions and data include all formsof non-volatile storage area. Non-transitory machine-readable storagemedia include, for example, semiconductor storage area devices, forexample, erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), and flash storage areadevices. Non-transitory machine-readable storage media include, forexample, magnetic disks such as internal hard disks or removable disks,magneto-optical disks, CD-ROMs (compact disk-read only memory) and DVD(digital versatile disk) ROM.

Each computing device may include a hard drive for storing data andcomputer programs, one or more processing devices (for example, amicroprocessor), and memory (for example, RAM) for executing computerprograms. Each computing device may include an image capture device,such as a still camera or video camera. The image capture device may bebuilt-in or simply accessible to the computing device.

Each computing device may include a graphics system, including a displayscreen. A display screen, such as a liquid crystal display (LCD) or aCRT (Cathode Ray Tube) displays to a user images that are generated bythe graphics system of the computing device. One or more displays orimages on a computer display (for example, a monitor) physicallytransforms the computer display. For example, if the computer display isLCD-based, the orientation of liquid crystals may be changed by theapplication of biasing voltages in a physical transformation that isvisually apparent to the user. As another example, if the computerdisplay is a CRT, the state of a fluorescent screen may be changed bythe impact of electrons in a physical transformation that is alsovisually apparent. Each display screen may be touch sensitive, allowinga user to enter information onto the display screen via a virtualkeyboard. On some computing devices, such as a desktop computer or asmartphone, a physical QWERTY keyboard or Arabic keyboard and scrollwheel may be provided for entering information onto the display screen.

Each computing device, and computer programs executed on each computingdevice, may also be configured to accept voice commands, and may beconfigured to perform functions in response to such commands. Forexample, the process described in this specification may be initiated ata client, to the extent possible, via voice commands.

Elements of different implementations described may be combined to formother implementations not specifically set forth previously. Elementsmay be left out of the processes described without adversely affectingtheir operation or the operation of the system in general. Furthermore,various separate elements may be combined into one or more individualelements to perform the functions described in this specification.

Other implementations not specifically described in this specificationare also within the scope of the following claims.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the principles of the present embodiments.

Certain Definitions

In order for the present disclosure to be more readily understood,certain terms are first defined below. Additional definitions for thefollowing terms and other terms are set forth throughout thespecification.

An apparatus, composition, or method described herein as “comprising”one or more named elements or steps is open-ended, meaning that thenamed elements or steps are essential, but other elements or steps maybe added within the scope of the composition or method. To avoidprolixity, it is also understood that any apparatus, composition, ormethod described as “comprising” (or which “comprises”) one or morenamed elements or steps also describes the corresponding, more limitedcomposition or method “consisting essentially of” (or which “consistsessentially of”) the same named elements or steps, meaning that thecomposition or method includes the named essential elements or steps andmay also include additional elements or steps that do not materiallyaffect the basic and novel characteristic(s) of the composition ormethod. It is also understood that any apparatus, composition, or methoddescribed herein as “comprising” or “consisting essentially of” one ormore named elements or steps also describes the corresponding, morelimited, and closed-ended composition or method “consisting of” (or“consists of”) the named elements or steps to the exclusion of any otherunnamed element or step. In any composition or method disclosed herein,known or disclosed equivalents of any named essential element or stepmay be substituted for that element or step.

As used herein, the term “wearable device” may include any device thatis able to be worn on the clothing or body of a person, oralternatively, any device that is able to be carried by a person in ahands free manner (for example, in a pocket, backpack, messenger bag,pouch, on a belt or strap, in a holder, or within another wearabledevice or piece of clothing), or any device that is able to be attachedto a machine, vehicle, or piece of equipment, without interfering theoperation of the machine vehicle or equipment. As used herein, the termmobile tracking device may refer to wearable devices, RFID tags, motionsensors, boundary markers, and other tracking sensors used to track themotion of a person or an object.

As used herein, the term “real-time” may describe devices and systemsthat track and update information within about 1 second (+/−0.2 seconds)from when the event is actually occurring. For example, real-timeposition tracking systems receive and update the position of a person,vehicle, equipment, or device within about 1 second from when thenmovement or movements of the person, vehicle, equipment, or device areactually occurring.

As used herein, a transmitter that is transmitting “continuously,”transmits at least one signal at least once per second.

As used herein, a receiver that is receiving “continuously,” receives atleast one signal at least once per second.

As used herein, a transceiver that is transmitting, receiving, or bothtransmitting and receiving “continuously,” transmits, receives, or bothtransmits and receives at least one signal at least once per second.

As used herein, an algorithm that is running “continuously,” updates atleast once per second.

As used herein, the term “near real-time” may describe devices andsystems that track and update information within about 20 seconds (+/−4seconds) from when the event is actually occurring. For example, nearreal-time position tracking systems receive and update the position of aperson, vehicle, equipment, or device within about 20 seconds from whenthen movement or movements of the person, vehicle, equipment, or deviceare actually occurring.

As used herein, the terms “neural network” and “correlation matrix” maybe used interchangeably and may refer to systems and methods that relateat least one input parameter to at least one output parameter of asystem, and quantify such relationships between input and outputparameters. Neural networks and correlation matrices may be builtautonomously via one or more computer-implemented systems, and may alsobe built in connection with one or more human inputs.

As used herein, the terms “machine-learning”, “artificial intelligence,”“cognitive reasoning,” “autonomous systems,” “adaptive algorithms,” and“heuristics” may all describe systems, methods, protocols, andapparatuses that search for and establish correlations that are atpartially predictive of at least one output or result, at least somepercent of the time, without requiring previous programming orinstruction for every executable step, and without needing to be 100%predictive in every situation.

As used herein, the term “machine-authority” may include or refer tosystems, apparatuses, methods, and protocols that enable at least onedecision, action, or portion thereof to be carried out based on one ormore instructions from a computer system, without requiring interventionby a human.

As used herein, the term “human-authority” may include or refer tosystems, apparatuses, methods, and protocols that enable finaldecision-making to be performed by one or more human beings, even ifactions are being carried out or executed (at least in part) bymachine-based systems.

As used herein, the term “substantially” refers to the qualitativecondition of exhibiting total or near-total extent or degree of acharacteristic or property of interest.

EQUIVALENTS

It is to be understood that while the disclosure has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention(s). Other aspects, advantages, and modifications are withinthe scope of the claims.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the present embodiments, including making and using anydevices or systems and performing any incorporated methods. Thepatentable scope of the present embodiments is defined by the claims,and may include other examples that occur to those skilled in the art.Such other examples are intended to be within the scope of the claims ifthey include structural elements that do not differ from the literallanguage of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

What is claimed is:
 1. A machine-learning ecosystem comprising: at leastone data input comprising: at least one input parameter; and at leastone output parameter; at least one prediction model based on the atleast one data input and relating the at least one output parameter tothe at least one input parameter; at least one correlation module forbuilding the at least one prediction model and performing at least onethreshold check on the at least one prediction model to assess therobustness of the at least one prediction model; and a decision modulecommunicatively coupled to the correlation module, the decision modulereceiving the at least one prediction model from the correlation module,where, based on at least one verification check at the decision module,at least one of a confirmation, a deferral, and a rejection of the atleast one prediction model is sent from the decision module to thecorrelation module.
 2. The ecosystem of claim 1, where the at least oneverification check comprises calculating an aggregate score comprisingat least one or more of: an r-squared value, a confidence interval, anumber of data points within the at least one data input, a number ofdata inputs used by the correlation module, an underlying data qualityof the at least one data input, a curve-fitting equation, and a transferfunction.
 3. The ecosystem of claim 1, where the at least one predictionmodel comprises at least one recommendation, the at least onerecommendation proposing one or more actions to improve at least one of:a productivity of a worksite; and at least one safety metric of aworksite.
 4. The ecosystem of claim 1, further comprising at least onecommunications module communicatively coupled to both the decisionmodule and the at least one correlation module, the at least onecommunications module receiving the at least one data input from atleast one data source, where the at least one communications modulecomprises at least one tri-band transceiver for transmitting andreceiving data within three or more different frequency bands.
 5. Theecosystem of claim 4, where the at least one communications moduleperforms at least one pre-processing step on the at least one datainput, the at least one pre-processing step including at least one of:parsing the data input, collating the data input, characterizing thedata input, filtering the data input, and decompressing the data input.6. The ecosystem of claim 1, further comprising: a site supervisorcommunicatively coupled to the decision module, the site supervisorcomprising at least one control console comprising at least one humaninterface, where the site supervisor affirms at least one predictionmodel confirmed by the decision module.
 7. The ecosystem of claim 6,where the site supervisor gradually transitions from human-authority tomachine-authority as a confidence level of the at least one predictionmodel generated by the correlation module increases.
 8. The ecosystem ofclaim 1, further comprising: at least one communications modulecommunicatively coupled to both the decision module and the at least onecorrelation module; and at least one wearable device communicativelycoupled to the at least one communications module, where the at leastone data input comprises one or more data points form the at least onewearable device.
 9. The ecosystem of claim 8, where the at least onewearable device comprises: at least one toxicity sensor; and at leastone microphone.
 10. The ecosystem of claim 9, where the at least onewearable device comprises at least one of: a temperature sensor, anaccelerometer, a humidity sensor, a vibration tool, a heartrate monitor,a PLC, a USB port, a speaker, and a camera.
 11. The ecosystem of claim9, where the at least one microphone records verbal communications thatare transmitted by the at least one wearable device to the at least onecommunications module, and where the correlation module uses the verbalcommunications as metadata for refining the at least one predictionmodel.
 12. The ecosystem of claim 1, further comprising: at least onecommunications module communicatively coupled to both the decisionmodule and the correlation module; and at least one data warehousecommunicatively coupled to both the at least one communications moduleand the correlation module, where the at least one data warehousecomprises enterprise data from at least one worksite.
 13. The ecosystemof claim 1, the correlation module comprising at least one of: at leastone graphics processing unit (GPU), at least one field programmable gatearray (FPGA), and at least one application-specific integrated circuit(ASIC).
 14. The ecosystem of claim 13, the correlation module furthercomprising more than one application-specific integrated circuit (ASIC)disposed in a climate-controlled environment comprising a temperaturenot exceeding 95 degrees F., where at least one application-specificintegrated circuit (ASIC) of the more than one application-specificintegrated circuit (ASIC) accommodates an input power from about 500 Wto about 3000 W, and an input voltage from about 110V to about 240V. 15.The ecosystem of claim 1, the decision module comprising at least onecentral processing unit (CPU).
 16. A method of building a correlationmatrix comprising: providing, at a correlation module, at least one datainput comprising at least one input parameter and at least one outputparameter; building, at the correlation module, at least one correlationrelating the at least one output parameter to the at least one inputparameter; performing, at the correlation module, at least one thresholdcheck on the at least one correlation; making, at the correlationmodule, at least one recommendation based on the at least onecorrelation; transmitting the at least one recommendation to a decisionmodule; evaluating, at the decision module, the at least onerecommendation; transmitting feedback from the decision module to thecorrelation module, the feedback comprising at least one of aconfirmation, a rejection, and a deferral; and initiating at least oneaction based on the evaluating, at the decision module, the at least onerecommendation.
 17. The method of claim 16, where, the at least oneaction is directed to at least one of: improving the productivity of aworksite; and improving the safety of a worksite.
 18. The method ofclaim 16, further comprising deploying the correlation matrix at aworksite, where initiating at least one action comprises at least oneof: sending out an alert, dispatching one or more rescue crews to anemergency area of the worksite, reassigning crew to a different task atthe worksite, and repositioning equipment at the worksite.
 19. Themethod of claim 16, further comprising refining, at the correlationmodule, the at least one correlation based on the feedback received fromthe decision module.
 20. The method of claim 16, further comprising:refining, at the correlation module, the at least one correlation basedon the at least one data input, where the at least one data inputcomprises at least one verbal recording received from at least onewearable device.
 21. The method of claim 16, where evaluating, at thedecision module, the at least one recommendation further comprisesperforming at least one verification check at the at least one decisionmodule.
 22. A machine-learning ecosystem comprising: a correlationmodule for building at least one prediction model based on at least onedata input including at least one input parameter and at least oneoutput parameter, the at least one prediction model relating the atleast one output parameter to the at least one input parameter, thecorrelation module performing at least one threshold check on the atleast one prediction model to assess the robustness of the at least oneprediction model; and a decision module communicatively coupled to thecorrelation module, the decision module receiving the at least oneprediction model from the correlation module, where, based on at leastone verification check at the decision module, at least one of aconfirmation, a deferral, and a rejection of the at least one predictionmodel is sent from the decision module to the correlation module.